A Method for Learning Value Systems in Generative AI

Andr\'es Holgado-S\'anchez; Holger Billhardt; Sascha Ossowski

REVIEW 3 major objections 5 minor 300 references

A Method for Learning Value Systems in Generative AI

T0 review · 3 major / 5 minor · reviewed 2026-08-01 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read From prompt-response preference pairs alone, an algorithm learns both the values behind a user's choices and the weights they assign to each value.

desk verdict A useful, clearly written adaptation of value-system learning to LLM reward models, with a fixable theoretical typo and a real accuracy trade-off on PKU; worth refereeing. read the letter →

arxiv 2607.16903 v1 pith:AYAFYHOC submitted 2026-07-18 cs.CY cs.AIcs.CLcs.LG

Andrés Holgado-Sánchez , Holger Billhardt , Sascha Ossowski This is my paper · ORCID

classification cs.CYcs.AIcs.CLcs.LG

keywords valuesystemlearningmulti-objectiverewardmodelpreferencegenerativeAIalignmentgroundinglinearscalarizationexplainabilityRLHF

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper proposes that a person's value system—how much they care about honesty, safety, helpfulness, and so on—can be inferred from ordinary preference data rather than by asking users to state weights. It adapts a previously validated value-system learning method to generative AI: from prompt-response pairs labeled both per value and for overall preference, the VSL-RM algorithm simultaneously trains a multi-objective reward model (the grounding that scores each response's alignment with each value) and a linear weight vector over those values. The weights are constrained to the simplex and are directly interpretable. The algorithm deliberately prioritizes grounding accuracy before value-system estimation, so the learned weights rest on a coherent value representation. On two preference datasets, VSL-RM matches or nearly matches a single-objective preference-learning baseline on overall preference prediction while producing explainable value-system weights.

What carries the argument

The central object is the value system function A_W,R(τ)=W·R(τ), a linear scalarization of a multi-objective reward vector R with weights W constrained to the unit simplex. It is implemented as a linear layer fed by reward heads over a frozen LLM backbone. Training uses a constrained Lagrangian objective—per-value loss targets, Lagrange multipliers, gradient descent-ascent—that keeps grounding discordance low before optimizing the value-system loss, plus a reward-centering term that keeps scales comparable across values.

What would settle it

Construct or find a set of prompt-response triples where a user's preferences form a cycle or violate the interval-scale structure implied by a linear utility model, and show VSL-RM's recovered weights fail to predict held-out preferences at better than chance accuracy. Alternatively, generate synthetic data from a known nonlinear value system (e.g., lexicographic preferences or threshold-based values) and measure how far the recovered linear weights diverge from the generating model.

Watch

Extended reading notes

Core claim

The central claim is that value alignment can be separated into two learnable pieces: a multi-objective reward model R(s,a) that scores a generation's alignment with each value, and a value system function A_W,R(τ)=W·R(τ) that aggregates those scores through a non-negative simplex weight vector. The paper shows that both pieces can be learned jointly from pairwise preference data using a Bradley-Terry loss, where grounding losses act as constraints whose targets are dynamically estimated, and the value-system loss is minimized subject to those targets. The authors report that this joint learning matches the grounding accuracy of a sequential baseline (SEQ-RM) while slightly exceeding its val

Load-bearing premise

The load-bearing premise is that a real value system can be well approximated by a fixed linear combination of the per-value alignment scores—that is, preference orderings are representable as W·R(τ) for non-negative weights summing to one; if users' preferences are not linear in the learned scores, the recovered weights misrepresent their value system.

Editorial extensions

If this is right

Users no longer need to state value weights; both a grounding and a value system can be read off preference data, making value-system elicitation feasible at dataset scale.
The learned weight vector is interpretable: it states the aggregate importance of each value, enabling auditing and direct steering of model behavior by adjusting the weights.
Because the grounding function R is reusable, the same reward vector can be combined with different users' weights for multi-objective fine-tuning, allowing exploration around the learned value system.
Simultaneous learning beats sequential training for linear value systems, because it finds groundings that aggregate linearly; the paper's constrained formulation makes this principled rather than heuristic.
The strong-duality result for affine reward heads justifies the algorithm as a sound approximation to the nested optimization problem, not just an ad hoc procedure.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The paper's own results suggest the linearity assumption is the ceiling: on PKU-Align-Anything the gap vs. the single-objective baseline widens, exactly what one would expect if real preferences are not a fixed weighted sum of the measured value scores. A context-dependent or mixture-of-experts weight model—which the authors mention as future work—could be tested against these recovered weights.
If correct, the method turns any preference dataset into a transparent value-system statement; comparing the weights recovered from different datasets could serve as an audit tool for detecting which values a corpus implicitly over- or under-weights.
Because the learned weights aggregate over a hypothetical abstract agent, applying them to heterogeneous annotators risks majority bias; the authors themselves caution that the value-system reward should not be used as a single RLHF objective in such cases.

Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Two things to know up front. This is a well-scoped, honestly written extension of an existing value-system learning method to multi-objective reward models for LLMs, and it ships code. The genuinely new piece is the simultaneous constrained training: a Lagrangian objective with adaptive loss targets and gradient descent-ascent, so grounding learning is prioritized while the interpretable value-system weights are being fit. On UltraFeedback and PKU it roughly matches the sequential variant and stays close to the single-objective reward baseline, with the trade-off stated in the paper rather than hidden.

What the paper does well: the formal setup (Definitions 1–4) is clear, the algorithm is concrete with pseudocode and hyperparameters, and the limitations section is unusually honest. It explicitly says the linear value-system representation is an approximation, acknowledges that aggregating heterogeneous annotators can obscure minorities, and recommends against using W·R as a standalone RLHF objective on heterogeneous data. That is a more principled posture than most work here. Code, dataset splits, and wall-clock times are provided, so the empirical claims are checkable.

Soft spots, in rough order. First, Proposition 1 in Appendix A.2 is internally inconsistent as printed. It assumes the loss targets satisfy L*_vi < L**_vi, which is below the attainable optimum and makes the constraint set empty, while the proof uses L*_vi > L**_vi to invoke Slater. It looks like a sign typo, but as written it removes the “theoretically sound” support. Second, the load-bearing modeling assumption is the fixed linear scalarization in Definition 4. On PKU the value-system accuracy is 0.714 for VSL-RM versus 0.773 for BT-RM, a six-point gap, and the paper itself says value systems may not be linear in the learned scores. That does not disprove the method, but it directly limits the interpretability claim: if the true value system is not linear, the recovered W is a projection, not the value system. “Minimal trade-offs” is too strong; “competitive with an acknowledged accuracy loss” is closer. Third, the Armo-RM comparison picks epsilon thresholds post hoc to maximize that model’s accuracies. This actually biases matters in Armo’s favor, so it is not unfair, but the protocol should be clearer, ideally with fixed held-out thresholds.

These are real but patchable issues. The paper is an incremental contribution, not a field reshuffler, and it deserves a serious referee. I would send it out and ask for a revision fixing the proposition, adding a sensitivity analysis on thresholds, and either softening the trade-off claim or testing a slightly richer value-system model. I would cite it for the algorithm and its honest framing.

Referee Report

3 major / 5 minor

Summary. The paper proposes VSL-RM, a method for jointly learning (i) a multi-objective reward model that implements a grounding of several human values and (ii) an interpretable linear value-system weight vector, from pairwise prompt-response preference data annotated per value and for an overall (possibly aggregated) agent. The algorithm formulates the problem as a constrained optimization that dynamically prioritizes grounding losses over the value-system loss, and solves it with a modified gradient descent-ascent procedure on a Lagrangian with adaptively updated loss targets. Evaluation on UltraFeedback and PKU-Align-Anything compares VSL-RM with a single-objective BT reward model, a sequential variant, and Armo-RM. The paper reports that VSL-RM achieves value-system accuracy close to the single-objective baseline while substantially improving grounding accuracy, with the learned weights providing explainability.

Significance. If the method’s central claims hold, it would offer a transparent alternative to latent-personalization reward models: auditors and users could inspect the weights assigned to each value. The paper is commendably concrete: it ships source code, uses four seeds and statistical tests, and includes a thoughtful discussion of aggregation and heterogeneity limitations. I do not see a circularity problem; the method is an empirical fitting procedure built on stipulated definitions. However, the theoretical strong-duality justification is internally inconsistent and does not cover the actual non-convex objective, and the empirical comparison with Armo-RM is affected by test-set threshold selection. Moreover, on PKU-Align-Anything the linear value-system assumption itself appears violated, which weakens the interpretability claim for that dataset. These issues are substantial but fixable within the manuscript’s scope.

major comments (3)

[Appendix A.2, Proposition 1] The strong-duality proof has two internal problems. First, the value-system loss is not convex in the weight parameters ω because W_ω = softmax(ω) is neither affine nor convex in ω; the proof asserts convexity “with respect to its parameters” based only on reward differences being linear combinations of affine functions, which holds only for fixed ω. Second, Proposition 1 assumes L*_vi < L**_vi (targets strictly below the attainable optimum), which makes the feasible set empty, while the proof invokes Slater’s condition using L*_vi > L**_vi. The actual adaptive target update (Algorithm 1, line 22) decreases L* whenever the recent average loss is below it, so targets can enter the regime excluded by the proposition. As printed, the theoretical backing for the algorithm does not hold; it should be corrected to an above-attainment approximation or the algorithm should be presented as an emp
[Appendix A.3 (“Chosen ε”)] The thresholds ε for Armo-RM are selected by maximizing AGA and VSA on the test set: “we selected the values of ε that yielded maximum average grounding accuracy (AGA) and value system accuracy (VSA) over a predefined set of feasible values.” For VSL-RM and the other baselines, ε is fixed from dataset score granularity. This is a test-set selection procedure and biases Tables 1–2. Its impact is large: for UltraFeedback, using the same ε=0.25 as VSL-RM changes Armo-RM’s AGA from 0.826 to 0.767 and VSA from 0.754 to 0.586. The evaluation threshold should be selected on the validation split or by the same rule for all methods.
[Section 5.1, Table 2; Definition 4] The paper’s central interpretability claim is that the recovered W represents the agent’s value system. This requires the linear scalarization of Definition 4 to be a good approximation of the true value system on the evaluation data. On PKU-Align-Anything, VSL-RM’s VSA is 0.714±0.0009 versus 0.773±0.0008 for the single-objective BT-RM, and Section 5.1 states that “value systems may not easily be approximated as linear functions of the dataset value alignment scores.” Under this failure mode, the learned W is a projection onto a misspecified class and may misrepresent the relative importance of values. The abstract’s “minimal trade-offs” and the explainability benefit are therefore not supported on one of the two datasets. The paper should either restrict the claims, provide a linearity diagnostic, or compare against a nonlinear but still interpretable value-system model.

minor comments (5)

[Section 3.2, Definition 4] The notation W∈(0,1)^m with sum 1 is the open simplex; the text says “bounded in the unit simplex.” This is harmless but should be stated as the open simplex.
[Table 3 caption] The caption reads “in UltraFeedback (left) and PKU-Align-Anything (right)”, but the table appears to present a single block of nine weight columns. Please clarify which columns belong to which dataset.
[Appendix A.4 (Wall-clock times)] The paragraph on wall-clock times is confusing: “we deduce the fact that all baselines and VSL-RM share the same codebase affects these results.” If all methods share the same codebase, the comparison is fair; if not, the times are not comparable. Please rewrite.
[Section 5, Methods] The hyperparameters were selected by Bayesian search on PKU-Align-Anything with 10% of the epochs. It is unclear whether UltraFeedback used the same hyperparameters without retuning. Please state this explicitly.
[Section 5.1, Table 3] The conclusion that VSL-RM’s less radical weight distribution is “qualitatively better” is a subjective judgment; consider reporting a quantitative measure (e.g., entropy or a diversity index) or a small user study to support the claim.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: VSL-RM is a supervised fitting method whose value-system weights and accuracies are evaluated on held-out test data; the linearity assumption is an explicitly stated modeling approximation, not a result derived from itself.

full rationale

The paper's central chain is empirical, not derivational. Definition 4 postulates that a value system can be represented by a linear scalarization W·R(τ); Problem 2 and Problem 6 then define a nested discordance-minimization objective; Algorithm 1 fits R_θ and W_ω to per-value and overall preference labels. The reported VSA and AGA are computed on held-out test splits (Tables 1-2), so the learned weights are not 'predicted' from the same data used to define their target in a way that forces success. The self-citations to Holgado-Sánchez et al. (2025, 2026) are used to motivate the representation and the simultaneous-learning strategy, but the paper's own comparisons against BT-RM, SEQ-RM, and Armo-RM provide the actual evidence for the central claims, so the citations are not load-bearing in a circular sense. The paper also explicitly acknowledges the main modeling risk: Section 5.1 states that 'value systems may not easily be approximated as linear functions of the dataset value alignment scores,' and Section 6 concedes that 'static linear weights ... may put limits to the accuracy of modelling real value-based preferences.' That is a disclosed correctness/scope limitation, not a circularity. The Appendix A.2 proof of Proposition 1 contains an internal inconsistency (it assumes L*_vi < L**_vi but proves Slater's condition using L*_vi > L**_vi), which weakens the claimed theoretical backing; however, this is a mathematical flaw, not a reduction of the result to its own inputs. The PKU evaluation uses Armo-RM attribute scores as proxies for some value labels, which is a disclosed approximation and could bias the comparison, but it does not make the central method circular. Overall, the derivation is self-contained as an empirical fitting-and-evaluation procedure.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The paper introduces no new physical or conceptual entities; "grounding", "value system", and "social grounding" are taken from prior work. The main ledger entries are the linearity assumption and the post hoc evaluation thresholds, both of which affect the strength of the empirical claims.

free parameters (4)

Value-system weights W_ω = e.g., Helpfulness 0.096, Honesty 0.347 on UltraFeedback
Learned outputs of the algorithm rather than hand-set assumptions, but they are central to the claimed interpretable value-system representation.
Armo-RM evaluation thresholds ϵ = 0.1 (AGA) and 0.01 (VSA) on UltraFeedback; 0.1 and 0.0 on PKU
Selected post hoc to maximize Armo-RM's reported accuracies (Appendix A.3), making the comparison metric a free parameter.
Preference discretization thresholds ϵ = 0.25 for UltraFeedback; 0.5 for PKU-Align-Anything
Chosen as half the minimum non-zero score difference; these thresholds determine all qualitative training labels.
Loss-target schedule hyperparameters = η=0.9, u=20, λ0, γλ=0.005, etc.
Control the exponentially weighted minimum loss-target estimation and multiplier regularization; selected by design/Bayesian search rather than derived.

assumptions (5)

domain assumption An agent's value system can be approximated by a weighted linear scalarization of value-alignment scores with non-negative simplex weights.
Definition 4 and the discussion in Section 3.2; this is the weakest load-bearing assumption and is acknowledged as an approximation.
domain assumption Aggregated preference labels reflect a social grounding of values and the value system of an abstract agent.
Section 4.1 assumes consensus in value interpretation and implicitly aggregates possibly heterogeneous value systems; the authors acknowledge representativeness limitations.
domain assumption Pairwise labels derived from quantitative scores are faithful to the true preferences involved.
For PKU, the paper discards the dataset's qualitative value annotations as inconsistent and regenerates value labels from quantitative scores (Appendix A.4).
domain assumption Bradley-Terry / sigmoid preference probabilities are an adequate differentiable surrogate for weak-order preferences.
Used in Eq. 3 and throughout the loss functions; standard in RLHF, but still a modeling assumption.
ad hoc to paper Strong duality and convergence premises hold: affine reward model, dense parameter space, strictly feasible loss targets.
Appendix A.2 Proposition 1; the strict target inequality as printed is infeasible, and the paper concedes strong duality may fail for deep networks.

how reviews work

0 comments

Cite this review

Pith. "Pith review of A Method for Learning Value Systems in Generative AI." pith.science (2026). https://pith.science/paper/AYAFYHOC

@misc{pith2026260716903,
  author       = {Pith},
  title        = {Pith review of: A Method for Learning Value Systems in Generative AI},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AYAFYHOC}},
  note         = {Machine review of arXiv:2607.16903}
}

read the original abstract

Value-aware AI systems require explicit computational representations of human values (groundings) and their aggregation into value systems in order to align their decisions with ours. As such representations are difficult to elicit, value learning seeks to infer them by observing human behaviour. This work addresses the lack of grounded value learning methods in generative AI: existing approaches typically replicate human preferences without awareness of the multidimensional structure of value alignment, or lack principled value system elicitation methods. To address these gaps, we adapt a previously validated value system learning method to the generative AI setting, which, based on pairwise prompt-response preference data, simultaneously learns: i) an implementation of a grounding for a set of values given by a multi-objective reward model, and ii) a value system representation in the form of a weighted linear scalarization of the previous grounding model. To ensure that the learned value systems are based on coherent value representations, our algorithm dynamically prioritizes the grounding learning process. We evaluate the method against baselines and a contemporary method on prompt-response preference datasets. Results show competitive performance and minimal trade-offs against the baselines, while improving explainability.

Figures

Figures reproduced from arXiv: 2607.16903 by the authors.

**Figure 2.** Average grounding and value system accuracy [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

300 extracted references · 28 canonical work pages

[1]

The 9th International Workshop on Freight Transportation and Logistics (ODYSSEUS) , title =

L. The 9th International Workshop on Freight Transportation and Logistics (ODYSSEUS) , title =
[2]

XIII International Workshop on Locational Analysis and Related Problems (IWOLOCA) , title =

L. XIII International Workshop on Locational Analysis and Related Problems (IWOLOCA) , title =
[3]

Vehicle Routing Problem with Fair Profits and Time Windows (

L. Vehicle Routing Problem with Fair Profits and Time Windows (. 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC) , pages=. 2023 , organization=

2023
[4]

CEUR Proceedings, 12th Int

On balancing fairness and efficiency in routing of cooperative vehicle fleets , author=. CEUR Proceedings, 12th Int. Workshop on ATT 2022 co-located with IJCAI-ECAI 2022 , volume=

2022
[5]

2024 , journal =

How to achieve fair and efficient cooperative vehicle routing? , author =. 2024 , journal =

2024
[6]

2024 , month = oct, journal =

Dynamic, Fair, and Efficient Routing for Cooperative Autonomous Vehicle Fleets , author =. 2024 , month = oct, journal =

2024
[7]

Multi-Objective Deep Inverse Reinforcement Learning through Direct Weights and Rewards Estimation , year=

Kishikawa, Daiko and Arai, Sachiyo , booktitle=. Multi-Objective Deep Inverse Reinforcement Learning through Direct Weights and Rewards Estimation , year=
[8]

Comparison of distance and reinforcement-learning rules in social-influence models , volume =

Norberto M Grzywacz , doi =. Comparison of distance and reinforcement-learning rules in social-influence models , volume =. Neurocomputing , keywords =

Show all 300 references

[9]

Bezdek and Robert Ehrlich and William Full , keywords =

James C. Bezdek and Robert Ehrlich and William Full , keywords =. FCM: The fuzzy c-means clustering algorithm , journal =. 1984 , issn =. doi:https://doi.org/10.1016/0098-3004(84)90020-7 , url =

1984 doi
[10]

2017 , booktitle =

Shen, Shitian and Chi, Min , title =. 2017 , booktitle =

2017
[11]

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence,

Contextualized Point-of-Interest Recommendation , author =. Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence,. 2020 , month =

2020
[12]

2018 , booktitle =

Yang, Carl and Shi, Xiaolin and Luo, Jie and Han, Jiawei , title =. 2018 , booktitle =. doi:10.1145/3219819.3219821 , publisher =

2018
[13]

Zhang and Tengfei Liu and Leonard K.M

Peixian Chen and Nevin L. Zhang and Tengfei Liu and Leonard K.M. Poon and Zhourong Chen and Farhan Khawar , keywords =. Latent tree models for hierarchical topic detection , journal =. 2017 , issn =. doi:https://doi.org/10.1016/j.artint.2017.06.004 , url =

2017 doi
[14]

, title =

Khawar, Farhan and Zhang, Nevin L. , title =. 2019 , booktitle=. doi:10.1109/ICDE.2019.00156 , abstract =

2019
[15]

and Cortinhal, Maria Jo \ a o

Dias, Jos \'e G. and Cortinhal, Maria Jo \ a o. The SKM Algorithm: A K-Means Algorithm for Clustering Sequential Data. Advances in Artificial Intelligence -- IBERAMIA 2008. 2008

2008
[16]

2022 , booktitle =

Emami, Negar and Di Maio, Antonio and Braun, Torsten , title =. 2022 , booktitle =. doi:10.1109/WiMob55322.2022.9941547 , abstract =

2022
[17]

Multi-Criteria Recommender Systems , url =

YoungOk Adomavicius Gediminas and Kwon , city =. Multi-Criteria Recommender Systems , url =. Recommender Systems Handbook , pages =. doi:10.1007/978-1-4899-7637-6_25 , isbn =

doi
[18]

Predicting users' movie preference and rating behavior from personality and values , volume =

Euna Mehnaz Khan and Md Saddam Hossain Mukta and Mohammed Eunus Ali and Jalal Uddin Mahmud , doi =. Predicting users' movie preference and rating behavior from personality and values , volume =. ACM Transactions on Interactive Intelligent Systems , keywords =
[19]

Supervised clustering of label ranking data using label preference information , volume =

Mihajlo Grbovic and Nemanja Djuric and Shengbo Guo and Slobodan Vucetic , doi =. Supervised clustering of label ranking data using label preference information , volume =. Machine Learning , pages =
[20]

and Sucholutsky, Ilia and Griffiths, Thomas L

Wynn, Andrea H. and Sucholutsky, Ilia and Griffiths, Thomas L. , title =. Proceedings of the 38th International Conference on Neural Information Processing Systems , articleno =. 2024 , isbn =

2024
[21]

Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-Experts

Wang, Haoxiang and Xiong, Wei and Xie, Tengyang and Zhao, Han and Zhang, Tong. Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-Experts. Findings of the Association for Computational Linguistics: EMNLP 2024. 2024. doi:10.18653/v1/2024.findings-emnlp.620

2024 doi
[22]

S im CSE : Simple Contrastive Learning of Sentence Embeddings

Gao, Tianyu and Yao, Xingcheng and Chen, Danqi. S im CSE : Simple Contrastive Learning of Sentence Embeddings. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 2021. doi:10.18653/v1/2021.emnlp-main.552

2021 doi
[23]

Beyond One-Preference-Fits-All Alignment: Multi-Objective Direct Preference Optimization

Zhou, Zhanhui and Liu, Jie and Shao, Jing and Yue, Xiangyu and Yang, Chao and Ouyang, Wanli and Qiao, Yu. Beyond One-Preference-Fits-All Alignment: Multi-Objective Direct Preference Optimization. Findings of the Association for Computational Linguistics: ACL 2024. 2024. doi:10...

2024 doi
[24]

Proceedings of the 41st International Conference on Machine Learning , articleno =

Yang, Rui and Pan, Xiaoman and Luo, Feng and Qiu, Shuang and Zhong, Han and Yu, Dong and Chen, Jianshu , title =. Proceedings of the 41st International Conference on Machine Learning , articleno =. 2024 , publisher =

2024
[25]

Artificial Intelligence and the Problem of Control

Russell, Stuart. Artificial Intelligence and the Problem of Control. Perspectives on Digital Humanism. 2022

2022
[26]

2024 , eprint=

What are human values, and how do we align AI to them? , author=. 2024 , eprint=

2024
[27]

Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society , author=

Aligning Agent Policies with Preferences: Human-Centered Interpretable Reinforcement Learning , volume=. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society , author=. 2025 , month=. doi:10.1609/aies.v8i2.36668 , abstractNote=

2025 doi
[28]

Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society , author=

Aligning AI Systems with Human Values: A Method for Identifying and Specifying Values , volume=. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society , author=. 2025 , month=. doi:10.1609/aies.v8i3.36762 , abstractNote=

2025 doi
[29]

Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society , author=

Steerable Pluralism: Pluralistic Alignment via Few-Shot Comparative Regression , volume=. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society , author=. 2025 , month=. doi:10.1609/aies.v8i1.36527 , abstractNote=

2025 doi
[30]

Modular Pluralism: Pluralistic Alignment via Multi- LLM Collaboration

Feng, Shangbin and Sorensen, Taylor and Liu, Yuhan and Fisher, Jillian and Park, Chan Young and Choi, Yejin and Tsvetkov, Yulia. Modular Pluralism: Pluralistic Alignment via Multi- LLM Collaboration. Proceedings of the 2024 Conference on Empirical Methods in Natural Language P...

2024 doi
[31]

The Value Learning Problem , year =

Nate Soares , booktitle =. The Value Learning Problem , year =
[32]

Moral Values in Norm Decision Making , year =

Serramia, Marc and L. Moral Values in Norm Decision Making , year =. Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems , pages =
[33]

R. M. Chisholm , journal =. Supererogation and Offence: A Conceptual Scheme for Ethics , volume =
[34]

Artificial Intelligence, Values, and Alignment , volume =

Iason Gabriel , doi =. Artificial Intelligence, Values, and Alignment , volume =. Minds and Machines , keywords =
[35]

Ethics Guidelines for Trustworthy AI , year =
[36]

2023 , doi =

Nieves Montes and Nardine Osman and Carles Sierra and Marija Slavkovik , title =. 2023 , doi =. arxiv , journal =:2302.08759 , timestamp =

2023 arXiv
[37]

, title =

Abbeel, Pieter and Ng, Andrew Y. , title =. Proceedings of the Twenty-First International Conference on Machine Learning , pages =. 2004 , isbn =. doi:10.1145/1015330.1015430 , abstract =

2004
[38]

Springer Berlin Heidelberg

Kostas and Nørvåg Kjetil and Kriegel Hans-Peter Ntoutsi Eirini and Stefanidis , publisher="Springer Berlin Heidelberg", address="Berlin, Heidelberg", editor =. Fast Group Recommendations by Applying User Clustering , year =. Conceptual Modeling , pages =
[39]

Proceedings of the AAAI Conference on Artificial Intelligence , author=

Incorporating Behavioral Constraints in Online AI Systems , volume=. Proceedings of the AAAI Conference on Artificial Intelligence , author=. 2019 , month=. doi:10.1609/aaai.v33i01.33013 , abstractNote=

2019 doi
[40]

Transparent Value Alignment , year =

Lindsay Sanneman and Julie Shah , doi =. Transparent Value Alignment , year =. ACM/IEEE International Conference on Human-Robot Interaction , keywords =
[41]

An Efficient, Generalized

Malik, Dhruv and Palaniappan, Malayandi and Fisac, Jaime and Hadfield-Menell, Dylan and Russell, Stuart and Dragan, Anca , booktitle =. An Efficient, Generalized. 2018 , editor =

2018
[42]

SemEval-2023 Task 4: ValueEval: Identification of Human Values Behind Arguments , publisher=

Johannes Kiesel and Milad Alshomary and Nailia Mirzakhmedova and Maximilian Heinrich and Nicolas Handke and Henning Wachsmuth and Benno Stein , editor =. SemEval-2023 Task 4: ValueEval: Identification of Human Values Behind Arguments , publisher=. 17th International Workshop o...

2023
[43]

and Russell, Stuart J

Ng, Andrew Y. and Russell, Stuart J. , title =. Proceedings of the Seventeenth International Conference on Machine Learning , pages =. 2000 , isbn =

2000
[44]

Inferring Values via Hybrid Intelligence , volume =

Enrico Liscio and Roger Lera-Leri and Filippo Bistaffa and Roel I J Dobbe and Catholijn M Jonker and Maite L. Inferring Values via Hybrid Intelligence , volume =. doi:10.3233/FAIA230102 , booktitle =

doi
[45]

Transparent Value Alignment , year =

Lindsay Sanneman and Julie Shah , doi =. Transparent Value Alignment , year =. ACM/IEEE International Conference on Human-Robot Interaction , pages =
[46]

VALUE SENSITIVE DESIGN in the DEVELOPMENT of DRIVERLESS VEHICLES: A CASE STUDY on AN AUTONOMOUS FAMILY VEHICLE , volume =

R Graubohm and T Schräder and M Maurer , doi =. VALUE SENSITIVE DESIGN in the DEVELOPMENT of DRIVERLESS VEHICLES: A CASE STUDY on AN AUTONOMOUS FAMILY VEHICLE , volume =. Proceedings of the Design Society: DESIGN Conference , pages =
[47]

Frontiers in Neurorobotics , title =

Shangding Gu and Alap Kshirsagar and Yali Du and Guang Chen and Jan Peters and Alois Knoll , doi =. Frontiers in Neurorobotics , title =
[48]

Value-based retweet prediction on twitter , volume =

Surbhi Kakar and Deepali Dhaka and Monica Mehrotra , doi =. Value-based retweet prediction on twitter , volume =. Informatica (Slovenia) , pages =
[49]

and Jonker, Catholijn M

Liscio, Enrico and van der Meer, Michiel and Siebert, Luciano C. and Jonker, Catholijn M. and Mouter, Niek and Murukannaiah, Pradeep K. , title =. Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems , pages =. 2021 , isbn =

2021
[50]

Autonomous Agents and Multi-Agent Systems , title =

Enrico Liscio and Michiel van der Meer and Luciano C Siebert and Catholijn M Jonker and Pradeep K Murukannaiah , doi =. Autonomous Agents and Multi-Agent Systems , title =
[51]

Sinnott-Armstrong, Walter , title =. The. 2023 , edition =

2023
[52]

Environmental dilemma game to establish a sustainable society dealing with an emergent value system , volume =

Jun Tanimoto , doi =. Environmental dilemma game to establish a sustainable society dealing with an emergent value system , volume =. Physica D: Nonlinear Phenomena , pages =
[53]

Algorithmic bias and the value sensitive design approach , volume =

Judith Simon and Pak-Hang Wong and Gernot Rieder , doi =. Algorithmic bias and the value sensitive design approach , volume =. Internet Policy Review , pages =
[54]

Proceedings of the 30th International Conference on Neural Information Processing Systems , pages =

Hadfield-Menell, Dylan and Dragan, Anca and Abbeel, Pieter and Russell, Stuart , title =. Proceedings of the 30th International Conference on Neural Information Processing Systems , pages =. 2016 , isbn =

2016
[55]

ALA 2021 - Adaptive and Learning Agents Workshop at AAMAS 2021 , title =

Manel Rodr. ALA 2021 - Adaptive and Learning Agents Workshop at AAMAS 2021 , title =

2021
[56]

Learning Ship Activity Patterns in Maritime Data Streams: Enhancing CEP Rule Learning by Temporal and Spatial Relations and Domain-Specific Functions , year=

Bruns, Ralf and Dunkel, Jürgen and Seremet, Serif , journal=. Learning Ship Activity Patterns in Maritime Data Streams: Enhancing CEP Rule Learning by Temporal and Spatial Relations and Domain-Specific Functions , year=
[57]

Proceedings of the 11th ACM International Conference on Distributed and Event-Based Systems , pages =

Mousheimish, Raef and Taher, Yehia and Zeitouni, Karine , title =. Proceedings of the 11th ACM International Conference on Distributed and Event-Based Systems , pages =. 2017 , isbn =. doi:10.1145/3093742.3093917 , abstract =

2017
[58]

ACM Comput

Gajcin, Jasmina and Dusparic, Ivana , title =. ACM Comput. Surv. , month =. 2024 , issue_date =. doi:10.1145/3648472 , abstract =

2024 doi
[59]

Instilling moral value alignment by means of multi-objective reinforcement learning , volume =

Rodr. Instilling moral value alignment by means of multi-objective reinforcement learning , volume =. Ethics and Information Technology , month =. doi:10.1007/s10676-022-09635-0 , issn =

doi
[60]

User Study Design for Identifying the Semantics of Bioethical Principles

Rodr \'i guez-Soto, Manel and Osman, Nardine and Sierra, Carles and Montes, Nieves and Martinez Roldan, Jordi and Cintas Garc \'i a, Rocio and Farriols Danes, Cristina and Garc \'i a Retortillo, Montserrat and Minguez Maso, Silvia. User Study Design for Identifying the Semanti...

2025 doi
[61]

Value Inference in Sociotechnical Systems , year =

Enrico Liscio and Roger Lera-Leri and Filippo Bistaffa and Roel I J Dobbe and Catholijn M Jonker and Maite L. Value Inference in Sociotechnical Systems , year =. Proc. AAMAS, Blue Sky Ideas Track , pages =
[62]

Computers in Human Behavior Reports , title =

Andreas Skalkos and Aggeliki Tsohou and Maria Karyda and Spyros Kokolakis , doi =. Computers in Human Behavior Reports , title =
[63]

and Manasrah, Ahmad and Alia, Mohammad , TITLE =

Jannoud, Ismael and Jaradat, Yousef and Masoud, Mohammad Z. and Manasrah, Ahmad and Alia, Mohammad , TITLE =. Electronics , VOLUME =. 2022 , NUMBER =

2022
[64]

2004 , publisher=

Convex optimization , author=. 2004 , publisher=

2004
[65]

Do personal values explain variation in satisficing measures of risk? , volume =

Kavitha Ranganathan , doi =. Do personal values explain variation in satisficing measures of risk? , volume =. Management Decision , pages =
[66]

2022 , eprint=

Convergence rate analysis of the gradient descent-ascent method for convex-concave saddle-point problems , author=. 2022 , eprint=

2022
[67]

Boyd, Stephen and Parikh, Neal and Chu, Eric and Peleato, Borja and Eckstein, Jonathan , title =. Found. Trends Mach. Learn. , month = jan, pages =. 2011 , issue_date =. doi:10.1561/2200000016 , abstract =

2011 doi
[68]

2017 , doi =

Beck, Amir , title =. 2017 , doi =

2017
[69]

An Optimal Multistage Stochastic Gradient Method for Minimax Problems , year=

Fallah, Alireza and Ozdaglar, Asuman and Pattathil, Sarath , booktitle=. An Optimal Multistage Stochastic Gradient Method for Minimax Problems , year=. doi:10.1109/CDC42340.2020.9304033 , ISSN=

2020
[70]

2018 , eprint=

On the Fenchel Duality between Strong Convexity and Lipschitz Continuous Gradient , author=. 2018 , eprint=

2018
[71]

and Jiao, Jiantao , title =

Zhu, Banghua and Jordan, Michael I. and Jiao, Jiantao , title =. Proceedings of the 41st International Conference on Machine Learning , articleno =. 2024 , publisher =

2024
[72]

Estimating Value Preferences in a Hybrid Participatory System , volume =

Luciano C Siebert and Enrico Liscio and Pradeep K Murukannaiah and Lionel Kaptein and Shannon Spruit and Jeroen Van Den Hoven and Catholijn Jonker , doi =. Estimating Value Preferences in a Hybrid Participatory System , volume =. Frontiers in Artificial Intelligence and Applic...
[73]

Aligning to Social Norms and Values in Interactive Narratives , year =

Prithviraj Ammanabrolu and Liwei Jiang and Maarten Sap and Hannaneh Hajishirzi and Yejin Choi , booktitle =. Aligning to Social Norms and Values in Interactive Narratives , year =
[74]

Amir and Liao, Qi and Malanchini, Ilaria and Widmer, Joerg , journal=

Bui, Nicola and Cesana, Matteo and Hosseini, S. Amir and Liao, Qi and Malanchini, Ilaria and Widmer, Joerg , journal=. A Survey of Anticipatory Mobile Networking: Context-Based Classification, Prediction Methodologies, and Optimization Techniques , year=
[75]

Enhancing Business Analytics with Tabular Data Synthesis via Context-Based Diffusion Models , year=

Liu, Changshuo , booktitle=. Enhancing Business Analytics with Tabular Data Synthesis via Context-Based Diffusion Models , year=
[76]

Enabling Classifiers to Make Judgements Explicitly Aligned with Human Values , year =

Yejin Bang and Tiezheng Yu and Andrea Madotto and Zhaojiang Lin and Mona Diab and Pascale Fung , editor =. Enabling Classifiers to Make Judgements Explicitly Aligned with Human Values , year =. Proceedings of the Annual Meeting of the Association for Computational Linguistics ...
[77]

LoRe: Personalizing

Avinandan Bose and Zhihan Xiong and Yuejie Chi and Simon Shaolei Du and Lin Xiao and Maryam Fazel , booktitle=. LoRe: Personalizing. 2025 , url=

2025
[78]

2025 , url=

Daiwei Chen and Yi Chen and Aniket Rege and Zhi Wang and Ramya Korlakai Vinayak , booktitle=. 2025 , url=

2025
[79]

Gradient-Adaptive Policy Optimization: Towards Multi-Objective Alignment of Large Language Models

Li, Chengao and Zhang, Hanyu and Xu, Yunkun and Xue, Hongyan and Ao, Xiang and He, Qing. Gradient-Adaptive Policy Optimization: Towards Multi-Objective Alignment of Large Language Models. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (...

2025 doi
[80]

Proceedings of the AAAI Conference on Artificial Intelligence , author=

Robust Multi-Objective Preference Alignment with Online DPO , volume=. Proceedings of the AAAI Conference on Artificial Intelligence , author=. 2025 , month=. doi:10.1609/aaai.v39i26.34942 , abstractNote=

2025 doi
[81]

2023 , issn =

A deep inverse reinforcement learning approach to route choice modeling with context-dependent rewards , journal =. 2023 , issn =. doi:10.1016/j.trc.2023.104079 , author =

2023
[82]

Engineering Normative and Cognitive Agents with Emotions and Values , volume =

Sz-Ting Tzeng , booktitle =. Engineering Normative and Cognitive Agents with Emotions and Values , volume =
[83]

Identification of human values from goal models , year =

Tahira Iqbal and Kuldar Taveter and Tarmo Strenze and Waqar Hussain and Omar Haggag and John Alphonsus Matthews and Anu Piirisild , doi =. Identification of human values from goal models , year =. Proceedings - 2024 IEEE/ACM 17th International Conference on Cooperative and Hum...

2024
[84]

Computers and Education , title =

Ashwin Mehta and Neil P Morris and Bronwen Swinnerton and Matt Homer , doi =. Computers and Education , title =
[85]

STAY MORAL AND EXPLORE: LEARN TO BEHAVE MORALLY IN TEXT-BASED GAMES , publisher=

Zijing Shi and Meng Fang and Yunqiu Xu and Ling Chen and Yali Du , booktitle =. STAY MORAL AND EXPLORE: LEARN TO BEHAVE MORALLY IN TEXT-BASED GAMES , publisher=
[86]

Towards value-sensitive learning analytics design , year =

Bodong Chen and Haiyi Zhu , doi =. Towards value-sensitive learning analytics design , year =. ACM International Conference Proceeding Series , pages =
[87]

Embedding stakeholder values in the requirements engineering process , volume =

Maaike Harbers and Christian Detweiler and Mark A Neerincx , doi =. Embedding stakeholder values in the requirements engineering process , volume =. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformat...
[88]

Scientific Reports , title =

Mehdi Khamassi and Marceau Nahon and Raja Chatila , doi =. Scientific Reports , title =
[89]

Adapting a kidney exchange algorithm to align with human values , year =

Rachel Freedman and John P Dickerson and Jana Schaich Borg and Walter Sinnott-Armstrong and Vincent Conitzer , booktitle =. Adapting a kidney exchange algorithm to align with human values , year =
[90]

Moral Gridworlds: A Theoretical Proposal for Modeling Artificial Moral Cognition , volume =

Julia Haas , doi =. Moral Gridworlds: A Theoretical Proposal for Modeling Artificial Moral Cognition , volume =. Minds and Machines , pages =
[91]

Learning Sparse Representations of Preferences within Choquet Expected Utility Theory , year =

Margot Herin and Patrice Perny and Nataliya Sokolovska , booktitle =. Learning Sparse Representations of Preferences within Choquet Expected Utility Theory , year =
[92]

2018 , eprint=

Scalable agent alignment via reward modeling: a research direction , author=. 2018 , eprint=

2018
[93]

Proceedings of the 37th International Conference on Machine Learning , pages =

Learning Human Objectives by Evaluating Hypothetical Behavior , author =. Proceedings of the 37th International Conference on Machine Learning , pages =. 2020 , editor =

2020
[94]

2024 , eprint=

Provable Multi-Party Reinforcement Learning with Diverse Human Feedback , author=. 2024 , eprint=

2024
[95]

, title =

Muslimani, Calarina and Taylor, Matthew E. , title =. Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems , pages =. 2024 , isbn =

2024
[96]

Proceedings of the 38th International Conference on Machine Learning , pages =

PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training , author =. Proceedings of the 38th International Conference on Machine Learning , pages =. 2021 , editor =

2021
[97]

Efficient Sampling-Based Maximum Entropy Inverse Reinforcement Learning With Application to Autonomous Driving , year=

Wu, Zheng and Sun, Liting and Zhan, Wei and Yang, Chenyu and Tomizuka, Masayoshi , journal=. Efficient Sampling-Based Maximum Entropy Inverse Reinforcement Learning With Application to Autonomous Driving , year=. doi:10.1109/LRA.2020.3005126 , ISSN=

2020
[98]

Mobile multimedia: Identifying user values using the means-end theory , year =

Michael Leitner and Peter Wolkerstorfer and Reinhard Sefelin and Manfred Tscheligi , doi =. Mobile multimedia: Identifying user values using the means-end theory , year =. MobileHCI 2008 - Proceedings of the 10th International Conference on Human-Computer Interaction with Mobi...

2008
[99]

SUTNLP at SemEval-2023 Task 4: LG-Transformer for Human Value Detection , publisher=

Hamed Hematian Hemati and Sayed Hesam Alavian and Hossein Sameti and Hamid Beigy , editor =. SUTNLP at SemEval-2023 Task 4: LG-Transformer for Human Value Detection , publisher=. 17th International Workshop on Semantic Evaluation, SemEval 2023 - Proceedings of the Workshop , pages =

2023
[100]

ON THE SENSITIVITY OF REWARD INFERENCE TO MISSPECIFIED HUMAN MODELS , publisher=

Joey Hong and Kush Bhatia and Anca Dragan , booktitle =. ON THE SENSITIVITY OF REWARD INFERENCE TO MISSPECIFIED HUMAN MODELS , publisher=
[101]

Proceedings of the 34th International Conference on Neural Information Processing Systems , articleno =

Kalweit, Gabriel and Huegle, Maria and Werling, Moritz and Boedecker, Joschka , title =. Proceedings of the 34th International Conference on Neural Information Processing Systems , articleno =. 2020 , isbn =

2020
[102]

A survey of inverse reinforcement learning: Challenges, methods and progress , volume =

Saurabh Arora and Prashant Doshi , doi =. A survey of inverse reinforcement learning: Challenges, methods and progress , volume =. Artificial Intelligence , keywords =
[103]

Lauri Ingman at SemEval-2023 Task 4: A Chain Classifier for Identifying Human Values behind Arguments , publisher=

Spencer Paulissen and Caroline J Wendt , doi =. Lauri Ingman at SemEval-2023 Task 4: A Chain Classifier for Identifying Human Values behind Arguments , publisher=. 17th International Workshop on Semantic Evaluation, SemEval 2023 - Proceedings of the Workshop , pages =

2023
[104]

and Sreedhar, Makesh Narsimhan and Kuchaiev, Oleksii , title =

Wang, Zhilin and Dong, Yi and Delalleau, Olivier and Zeng, Jiaqi and Shen, Gerald and Egert, Daniel and Zhang, Jimmy J. and Sreedhar, Makesh Narsimhan and Kuchaiev, Oleksii , title =. Proceedings of the 38th International Conference on Neural Information Processing Systems , a...

2024
[105]

Can we replicate real human behaviour using artificial neural networks? , volume =

Georg Jäger and Daniel Reisinger , doi =. Can we replicate real human behaviour using artificial neural networks? , volume =. Mathematical and Computer Modelling of Dynamical Systems , pages =
[106]

SteerLM: Attribute Conditioned SFT as an (User-Steerable) Alternative to RLHF , year =

Yi Dong and Zhilin Wang and Makesh Narsimhan Sreedhar and Xianchao Wu and Oleksii Kuchaiev , booktitle =. SteerLM: Attribute Conditioned SFT as an (User-Steerable) Alternative to RLHF , year =
[107]

On Admissible Behaviours for Goal-Oriented Decision-Making of Value-Aware Agents

Holgado-S \'a nchez, Andr \'e s and Arias, Joaqu \'i n and Moreno-Rebato, Mar and Ossowski, Sascha. EUMAS , title="On Admissible Behaviours for Goal-Oriented Decision-Making of Value-Aware Agents", booktitle="Multi-Agent Systems", year="2023", publisher="Springer Nature Switze...

2023
[108]

Algorithms for Learning Value-Aligned Policies Considering Admissibility Relaxation

Holgado-S \'a nchez, Andr \'e s and Arias, Joaqu \'i n and Billhardt, Holger and Ossowski, Sascha. Algorithms for Learning Value-Aligned Policies Considering Admissibility Relaxation. Value Engineering in Artificial Intelligence. 2024

2024
[109]

Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: AWAI , year=

Andrés Holgado-Sánchez and Holger Billhardt and Sascha Ossowski and Alberto Fernández , title=. Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: AWAI , year=. doi:10.5220/0012595500003636 , isbn=

doi
[110]

Details withheld to preserve blind review

Author. Details withheld to preserve blind review. 2024

2024
[111]

Goal Alignment: Re-analyzing Value Alignment Problems Using Human-Aware AI , volume =

Malek Mechergui and Sarath Sreedharan , booktitle =. Goal Alignment: Re-analyzing Value Alignment Problems Using Human-Aware AI , volume =
[112]

Using ChatGPT to Generate Human-Value User Stories as Inspirational Triggers , year =

Agnieszka Marczak-Czajka and Jane Cleland-Huang , doi =. Using ChatGPT to Generate Human-Value User Stories as Inspirational Triggers , year =. Proceedings - 31st IEEE International Requirements Engineering Conference Workshops, REW 2023 , pages =

2023
[113]

2025 , eprint=

Instilling Organisational Values in Firefighters through Simulation-Based Training , author=. 2025 , eprint=

2025
[114]

Basic Human Values and Moral Foundations Theory in ValueNet Ontology

De Giorgis, Stefano and Gangemi, Aldo and Damiano, Rossana. Basic Human Values and Moral Foundations Theory in ValueNet Ontology. Knowledge Engineering and Knowledge Management. 2022. doi:10.1007/978-3-031-17105-5_1

2022 doi
[115]

Inverse Reward Design , url =

Hadfield-Menell, Dylan and Milli, Smitha and Abbeel, Pieter and Russell, Stuart J and Dragan, Anca , booktitle =. Inverse Reward Design , url =
[116]

2024 , eprint=

Helping or Herding? Reward Model Ensembles Mitigate but do not Eliminate Reward Hacking , author=. 2024 , eprint=

2024
[117]

SAFE RLHF: SAFE REINFORCEMENT LEARNING FROM HUMAN FEEDBACK , publisher =

Juntao Dai and Xuehai Pan and Ruiyang Sun and Jiaming Ji and Xinbo Xu and Mickel Liu and Yizhou Wang and Yaodong Yang , booktitle =. SAFE RLHF: SAFE REINFORCEMENT LEARNING FROM HUMAN FEEDBACK , publisher =
[118]

VALUENET: A New Dataset for Human Value Driven Dialogue System , volume =

Liang Qiu and Yizhou Zhao and Jinchao Li and Pan Lu and Baolin Peng and Jianfeng Gao and Song-Chun Zhu , booktitle =. VALUENET: A New Dataset for Human Value Driven Dialogue System , volume =
[119]

AI Alignment and Human Reward , year =

Patrick Butlin , doi =. AI Alignment and Human Reward , year =. AIES 2021 - Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society , pages =

2021
[120]

Different tubers for different consumers: Heterogeneity in human values and willingness to pay for social outcomes of potato credence attributes , volume =

Jill Fitzsimmons and Gianni Cicia , doi =. Different tubers for different consumers: Heterogeneity in human values and willingness to pay for social outcomes of potato credence attributes , volume =. International Journal on Food System Dynamics , pages =
[121]

Using demographic features for the prediction of basic human values underlying stakeholder motivation , volume =

Adam Szekeres and Pankaj Shivdayal Wasnik and Einar Arthur Snekkenes , doi =. Using demographic features for the prediction of basic human values underlying stakeholder motivation , volume =. ICEIS 2019 - Proceedings of the 21st International Conference on Enterprise Informati...

2019
[122]

and Le, Quoc V

Wei, Jason and Wang, Xuezhi and Schuurmans, Dale and Bosma, Maarten and Ichter, Brian and Xia, Fei and Chi, Ed H. and Le, Quoc V. and Zhou, Denny , title =. Proceedings of the 36th International Conference on Neural Information Processing Systems , articleno =. 2024 , isbn =

2024
[123]

Actionable ethics through neural learning , publisher=

Daniele Rossini and Danilo Croce and Sara Mancini and Massimo Pellegrino and Roberto Basili , booktitle =. Actionable ethics through neural learning , publisher=
[124]

and Leike, Jan and Brown, Tom B

Christiano, Paul F. and Leike, Jan and Brown, Tom B. and Martic, Miljan and Legg, Shane and Amodei, Dario , title =. 2017 , booktitle =

2017
[125]

Human-Value-Oriented Digital Social Innovation: A Multilevel Design Framework , year =

Larissa Gebken and Christian Kurtz and Paul Drews and Ingrid Schirmer and Tilo Böhmann , booktitle =. Human-Value-Oriented Digital Social Innovation: A Multilevel Design Framework , year =
[126]

, advisor =

Ziebart, Brian D. , advisor =. Modeling purposeful adaptive behavior with the principle of maximum causal entropy , year =. doi:10.1184/R1/6720692.v1 , school =

doi
[127]

Nonlinear inverse reinforcement learning with Gaussian processes , year =

Levine, Sergey and Popovi\'. Nonlinear inverse reinforcement learning with Gaussian processes , year =. Proceedings of the 25th International Conference on Neural Information Processing Systems , pages =
[128]

Rothkopf and Christos Dimitrakakis , doi =

Constantin A. Rothkopf and Christos Dimitrakakis , doi =. Preference elicitation and inverse reinforcement learning , volume =. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , keywords =
[129]

Maximum entropy inverse reinforcement learning , year =

Brian D Ziebart and Andrew Maas and J Andrew Bagnell and Anind K Dey , isbn =. Maximum entropy inverse reinforcement learning , year =. Proceedings of the 23rd National Conference on Artificial Intelligence - Volume 3 , pages =
[130]

Maximum Entropy Deep Inverse Reinforcement Learning , journal =

Markus Wulfmeier and Peter Ondrúška and Ondr´ Ondrúška and Ingmar Posner , month =. Maximum Entropy Deep Inverse Reinforcement Learning , journal =. doi:10.48550/arXiv.1507.04888 , year =

doi arXiv
[131]

Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems , pages =

Aguilera, Alba and Montes, Nieves and Curto, Georgina and Sierra, Carles and Osman, Nardine , title =. Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems , pages =. 2024 , isbn =

2024
[132]

Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization , year =

Chelsea Finn and Sergey Levine and Pieter Abbeel , issn =. Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization , year =
[133]

and Shen, Yiting and Mihalcea, Rada

Wilson, Steven R. and Shen, Yiting and Mihalcea, Rada. Building and Validating Hierarchical Lexicons with a Case Study on Personal Values. Social Informatics. 2018

2018
[134]

McKee and Richard Everett and Saffron Huang and Tina O

Laura Weidinger and Kevin R. McKee and Richard Everett and Saffron Huang and Tina O. Zhu and Martin J. Chadwick and Christopher Summerfield and Iason Gabriel , title =. Proceedings of the National Academy of Sciences , volume =. 2023 , doi =. https://www.pnas.org/doi/pdf/10.10...

2023 doi
[135]

Rodríguez-Aguilar , isbn =

Celeste Veronese and Daniele Meli and Filippo Bistaffa and Manel Rodríguez-Soto and Alessandro Farinelli and Juan A. Rodríguez-Aguilar , isbn =. Inductive Logic Programming for Transparent Alignment with Multiple Moral Values , volume =. CEUR workshop proceedings , address=
[136]

and Vosoughi, Soroush , title =

Liu, Ruibo and Jia, Chenyan and Zhang, Ge and Zhuang, Ziyu and Liu, Tony X. and Vosoughi, Soroush , title =. Proceedings of the 36th International Conference on Neural Information Processing Systems , articleno =. 2022 , isbn =

2022
[137]

Jonker and Maite L

Roger Xavier Lera-Leri and Enrico Liscio and Filippo Bistaffa and Catholijn M. Jonker and Maite L. Aggregating value systems for decision support , journal =. 2024 , doi =

2024
[138]

European Sociological Review , volume =

Luijkx, Ruud and Jónsdóttir, Guðbjörg Andrea and Gummer, Tobias and Ernst Stähli, Michèle and Frederiksen, Morten and Ketola, Kimmo and Reeskens, Tim and Brislinger, Evelyn and Christmann, Pablo and Gunnarsson, Stefán Þór and Hjaltason, Árni Bragi and Joye, Dominique and Lomaz...

2020
[139]

Cross-Domain Classification of Moral Values

Liscio, Enrico and Dondera, Alin and Geadau, Andrei and Jonker, Catholijn and Murukannaiah, Pradeep. Cross-Domain Classification of Moral Values. Findings of the Association for Computational Linguistics: NAACL 2022. 2022. doi:10.18653/v1/2022.findings-naacl.209

2022 doi
[140]

, booktitle=

Fogel, D.B. , booktitle=. Using evolutionary programing to create neural networks that are capable of playing tic-tac-toe , year=
[141]

Self-reflection on personal values to support value-sensitive design , year =

Alina Pommeranz and Christian Detweiler and Pascal Wiggers and Catholijn M Jonker , doi =. Self-reflection on personal values to support value-sensitive design , year =. Proceedings of HCI 2011 - 25th BCS Conference on Human Computer Interaction , pages =

2011
[142]

Proceedings of the 30th International Conference on Algorithmic Learning Theory , pages =

Two-Player Games for Efficient Non-Convex Constrained Optimization , author =. Proceedings of the 30th International Conference on Algorithmic Learning Theory , pages =. 2019 , editor =

2019
[143]

Entropy , VOLUME =

Nielsen, Frank , TITLE =. Entropy , VOLUME =. 2020 , NUMBER =

2020
[144]

Advances in Experimental Social Psychology , publisher =

Chapter Two - Moral Foundations Theory: The Pragmatic Validity of Moral Pluralism , editor =. Advances in Experimental Social Psychology , publisher =. 2013 , issn =. doi:10.1016/B978-0-12-407236-7.00002-4 , author =

2013 doi
[145]

and Littman, Michael L

Abel, David and Dabney, Will and Harutyunyan, Anna and Ho, Mark K. and Littman, Michael L. and Precup, Doina and Singh, Satinder , title =. Proceedings of the 35th International Conference on Neural Information Processing Systems , articleno =. 2021 , isbn =

2021
[146]

Andersson, Henrik and Hsieh, Nien-hê , title =. The. 2025 , edition =

2025
[147]

How Does Predictive Information Affect Human Ethical Preferences? , year =

Saumik Narayanan and Guanghui Yu and Wei Tang and Chien-Ju Ho and Ming Yin , doi =. How Does Predictive Information Affect Human Ethical Preferences? , year =. AIES 2022 - Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society , pages =

2022
[148]

Preference Ranking Optimization for Human Alignment , volume =

Feifan Song and Bowen Yu and Minghao Li and Haiyang Yu and Fei Huang and Yongbin Li and Houfeng Wang , doi =. Preference Ranking Optimization for Human Alignment , volume =. Proceedings of the 38th AAAI Conference on Artificial Intelligence , pages =
[149]

How to talk so AI will learn: Instructions, descriptions, and autonomy , url =

Sumers, Theodore and Hawkins, Robert and Ho, Mark K and Griffiths, Tom and Hadfield-Menell, Dylan , editor =. How to talk so AI will learn: Instructions, descriptions, and autonomy , url =. 2022 , booktitle =

2022
[150]

The Touché23-ValueEval Dataset for Identifying Human Values behind Arguments , year =

Nailia Mirzakhmedova and Johannes Kiesel and Milad Alshomary and Maximilian Heinrich and Nicolas Handke and Xiaoni Cai and Valentin Barriere and Doratossadat Dastgheib and Omid Ghahroodi and Mohammad Ali Sadraei Javaheri and Ehsaneddin Asgari and Lea Kawaletz and Henning Wachs...

2024
[151]

Journal of Machine Learning Research , year =

Mirco Mutti and Riccardo De Santi and Piersilvio De Bartolomeis and Marcello Restelli , title =. Journal of Machine Learning Research , year =
[152]

and Wen Sun

Wenhao Zhan and Masatoshi Uehara and Nathan Kallus and Lee, Jason D. and Wen Sun. PROVABLE OFFLINE PREFERENCE-BASED REINFORCEMENT LEARNING. 12th International Conference on Learning Representations, ICLR 2024 , year =

2024
[153]

2016 , eprint=

Prioritized Experience Replay , author=. 2016 , eprint=

2016
[154]

The Twelfth International Conference on Learning Representations , year=

Query-Policy Misalignment in Preference-Based Reinforcement Learning , author=. The Twelfth International Conference on Learning Representations , year=
[155]

International Conference on Learning Representations , publisher =

Learning Robust Rewards with Adverserial Inverse Reinforcement Learning , author=. International Conference on Learning Representations , publisher =. 2018 , url=

2018
[156]

and Siebert, Luciano C

Peschl, Markus and Zgonnikov, Arkady and Oliehoek, Frans A. and Siebert, Luciano C. , title =. Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems , pages =. 2022 , isbn =

2022
[157]

Morris , journal =

Max D. Morris , journal =. Factorial Sampling Plans for Preliminary Computational Experiments , urldate =
[158]

Identifying the Human Values behind Arguments , volume =

Johannes Kiesel and Milad Alshomary and Nicolas Handke and Xiaoni Cai and Henning Wachsmuth and Benno Stein , editor =. Identifying the Human Values behind Arguments , volume =. Proceedings of the Annual Meeting of the Association for Computational Linguistics , pages =
[159]

A qualitative approach to assess the alignment of Value Systems in collaborative enterprises networks , volume =

Patricia Macedo and Luis M Camarinha-Matos , doi =. A qualitative approach to assess the alignment of Value Systems in collaborative enterprises networks , volume =. Computers and Industrial Engineering , pages =
[160]

Analysis of core-values alignment in collaborative networks , volume =

Luis M Camarinha-Matos and Patrícia Macedo and António Abreu , doi =. Analysis of core-values alignment in collaborative networks , volume =. IFIP International Federation for Information Processing , pages =
[161]

The Inadequacy of Reinforcement Learning From Human Feedback - Radicalizing Large Language Models via Semantic Vulnerabilities , volume =

Timothy R Mcintosh and Teo Susnjak and Tong Liu and Paul Watters and Malka N Halgamuge , doi =. The Inadequacy of Reinforcement Learning From Human Feedback - Radicalizing Large Language Models via Semantic Vulnerabilities , volume =. IEEE Transactions on Cognitive and Develop...
[162]

C. F. Jeff Wu , title =. The Annals of Statistics , number =. 1983 , doi =

1983
[163]

Predicting demographics, moral foundations, and human values from digital behaviours , volume =

Kyriaki Kalimeri and Mariano G Beiró and Matteo Delfino and Robert Raleigh and Ciro Cattuto , doi =. Predicting demographics, moral foundations, and human values from digital behaviours , volume =. Computers in Human Behavior , pages =
[164]

Reflexive Design for Fairness and Other Human Values in Formal Models , year =

Benjamin Fish and Luke Stark , doi =. Reflexive Design for Fairness and Other Human Values in Formal Models , year =. AIES 2021 - Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society , pages =

2021
[165]

Operationalizing Human Values in Software Engineering: A Survey , volume =

Mojtaba Shahin and Waqar Hussain and Arif Nurwidyantoro and Harsha Perera and Rifat Shams and John Grundy and Jon Whittle , doi =. Operationalizing Human Values in Software Engineering: A Survey , volume =. IEEE Access , pages =
[166]

Inferring human values for safe AGI design , volume =

Can Eren Sezener , doi =. Inferring human values for safe AGI design , volume =. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , pages =
[167]

J. C. Dunn , title =. Journal of Cybernetics , volume =. 1974 , publisher =. doi:10.1080/01969727408546059 , eprint =

1974 doi
[168]

2017 , volume =

Foundations and Trends® in Human–Computer Interaction , title =. 2017 , volume =. doi:10.1561/1100000015 , issn =

2017 doi
[169]

Adam-Smith at SemEval-2023 Task 4: Discovering Human Values in Arguments with Ensembles of Transformer-based Models , year =

Daniel Schroter and Daryna Dementieva and Georg Groh , editor =. Adam-Smith at SemEval-2023 Task 4: Discovering Human Values in Arguments with Ensembles of Transformer-based Models , year =. 17th International Workshop on Semantic Evaluation, SemEval 2023 - Proceedings of the ...

2023
[170]

Elicitation of situated values: Need for tools to help stakeholders and designers to reflect and communicate , volume =

Alina Pommeranz and Christian Detweiler and Pascal Wiggers and Catholijn Jonker , doi =. Elicitation of situated values: Need for tools to help stakeholders and designers to reflect and communicate , volume =. Ethics and Information Technology , pages =
[171]

Saddam Hossain and Ali, Mohammed Eunus and Mahmud, Jalal , title =

Mukta, Md. Saddam Hossain and Ali, Mohammed Eunus and Mahmud, Jalal , title =. Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 , pages =. 2017 , isbn =. doi:10.1145/3110025.3110087 , abstract =

2017
[172]

Strategic decision-making processes: the role of management and context , volume =

Vassilis M Papadakis and Spyros Lioukas and David Chambers , doi =. Strategic decision-making processes: the role of management and context , volume =. Strategic Management Journal , keywords =
[173]

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , pages =

Fan, Wenqi and Ding, Yujuan and Ning, Liangbo and Wang, Shijie and Li, Hengyun and Yin, Dawei and Chua, Tat-Seng and Li, Qing , title =. Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , pages =. 2024 , isbn =. doi:10.1145/3637528.3671470 ,...

2024
[174]

Nova: Value-based Negotiation of Norms , year =

Aydo. Nova: Value-based Negotiation of Norms , year =. ACM Trans. Intell. Syst. Technol. , month = aug, articleno =. doi:10.1145/3465054 , abstract =

doi
[175]

2023 , issn =

A systematic review of value-aware recommender systems , journal =. 2023 , issn =. doi:https://doi.org/10.1016/j.eswa.2023.120131 , author =

2023
[176]

Inverse Norm Conflict Resolution , year =

Daniel Kasenberg and Matthias Scheutz , doi =. Inverse Norm Conflict Resolution , year =. AIES 2018 - Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society , pages =

2018
[177]

Inherent value systems for autonomous mental development , volume =

Xiao Huang and Juyang Weng , doi =. Inherent value systems for autonomous mental development , volume =. International Journal of Humanoid Robotics , pages =
[178]

Dynamics of Moral Behavior in Heterogeneous Populations of Learning Agents , volume =

Elizaveta Tennant and Stephen Hailes and Mirco Musolesi , doi =. Dynamics of Moral Behavior in Heterogeneous Populations of Learning Agents , volume =. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society , month =
[179]

Beyond Ethical Alignment : Evaluating LLMs as Artificial Moral Assistants , volume =

Galatolo, Alessio and Rappuoli, Luca Alberto and Winkle, Katie and Beloucif, Meriem , institution =. Beyond Ethical Alignment : Evaluating LLMs as Artificial Moral Assistants , volume =. Frontiers in Artificial Intelligence and Applications , pages =. doi:10.3233/faia250934 , ...

doi
[180]

Moral Alignment for

Elizaveta Tennant and Stephen Hailes and Mirco Musolesi , booktitle=. Moral Alignment for. 2025 , url=

2025
[181]

, author=

The emotional dog and its rational tail: a social intuitionist approach to moral judgment. , author=. Psychological review , volume=. 2001 , publisher=. doi:10.1037/0033-295X.108.4.814 , url=

2001 doi
[182]

Proceedings of the 26th International Joint Conference on Artificial Intelligence , pages =

Dignum, Virginia , title =. Proceedings of the 26th International Joint Conference on Artificial Intelligence , pages =. 2017 , isbn =

2017
[183]

2021 , isbn =

Peschl, Markus , title =. 2021 , isbn =. doi:10.1145/3461702.3462473 , booktitle =

2021
[184]

Complex value systems in friendly AI , volume =

Eliezer Yudkowsky , doi =. Complex value systems in friendly AI , volume =. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , pages =
[185]

Towards a conceptual model of value systems in collaborative networks , volume =

Luis M Camarinha-matos and Patrícia Macedo , doi =. Towards a conceptual model of value systems in collaborative networks , volume =. IFIP International Federation for Information Processing , pages =
[186]

Overview of Touché 2024: Argumentation Systems , volume =

Johannes Kiesel and Çağrı Çöltekin and Maximilian Heinrich and Maik Fröbe and Milad Alshomary and Bertrand De Longueville and Tomaž Erjavec and Nicolas Handke and Matyáš Kopp and Nikola Ljubešić and Katja Meden and Nailia Mirzhakhmedova and Vaidas Morkevičius and Theresa Reiti...

2024
[187]

Rudolf Christoph Eucken at SemEval-2023 Task 4: An Ensemble Approach for Identifying Human Values from Arguments , year =

Sougata Saha and Rohini Srihari , editor =. Rudolf Christoph Eucken at SemEval-2023 Task 4: An Ensemble Approach for Identifying Human Values from Arguments , year =. 17th International Workshop on Semantic Evaluation, SemEval 2023 - Proceedings of the Workshop , pages =

2023
[188]

ALIGNING AI WITH SHARED HUMAN VALUES , year =

Dan Hendrycks and Collin Burns and Steven Basart and Andrew Critch and Jerry Li and Dawn Song and Jacob Steinhardt , booktitle =. ALIGNING AI WITH SHARED HUMAN VALUES , year =
[189]

Metric learning for value alignment , volume =

Andrea Loreggia and Nicholas Mattei and Francesca Rossi and K Brent Venable , booktitle =. Metric learning for value alignment , volume =
[190]

Proceedings of the National Academy of Sciences of the United States of America , title =

Cameron A Hecht and Christopher J Bryan and David S Yeager , doi =. Proceedings of the National Academy of Sciences of the United States of America , title =
[191]

Hitachi at SemEval-2023 Task 4: Exploring Various Task Formulations Reveals the Importance of Description Texts on Human Values , year =

Masaya Tsunokake and Atsuki Yamaguchi and Yuta Koreeda and Hiroaki Ozaki and Yasuhiro Sogawa , doi =. Hitachi at SemEval-2023 Task 4: Exploring Various Task Formulations Reveals the Importance of Description Texts on Human Values , year =. 17th International Workshop on Semant...

2023
[192]

Is There a Need to Address Human Values in Domain Modelling? , year =

Gunter Mussbacher and Waqar Hussain and Jon Whittle , doi =. Is There a Need to Address Human Values in Domain Modelling? , year =. Proceedings - 10th International Model-Driven Requirements Engineering Workshop, MoDRE 2020 , pages =

2020
[193]

FutureMatch: Combining human value judgments and machine learning to match in dynamic environments , volume =

John P Dickerson and Tuomas Sandholm , booktitle =. FutureMatch: Combining human value judgments and machine learning to match in dynamic environments , volume =
[194]

An Automatic Dictionary Extraction and Annotation Method Using Simulated Annealing for Detecting Human Values , year =

Yasuhiro Takayama and Yoichi Tomiura and Kenneth R Fleischmann and An-Shou Cheng and Douglas W Oard and Emi Ishita , doi =. An Automatic Dictionary Extraction and Annotation Method Using Simulated Annealing for Detecting Human Values , year =. Proceedings - 2015 IIAI 4th Inter...

2015
[195]

VAPBr: Values in Digital Games for Public Service in Brazil , volume =

Fabrício Janssen and Renata Mendes de Araujo and Tadeu Moreira de Classe , doi =. VAPBr: Values in Digital Games for Public Service in Brazil , volume =. International Journal of Serious Games , pages =
[196]

Predicting CEO misbehavior from observables: Comparative evaluation of two major personality models , volume =

Adam Szekeres and Einar Arthur Snekkenes , doi =. Predicting CEO misbehavior from observables: Comparative evaluation of two major personality models , volume =. Communications in Computer and Information Science , pages =
[197]

SMASH: A Semantic-Enabled Multi-agent Approach for Self-adaptation of Human-Centered IoT

Rahimi, Hamed and Trentin, Iago Felipe and Ramparany, Fano and Boissier, Olivier. SMASH: A Semantic-Enabled Multi-agent Approach for Self-adaptation of Human-Centered IoT. Advances in Practical Applications of Agents, Multi-Agent Systems, and Social Good. The PAAMS Collection. 2021

2021
[198]

-SMASH: Q-Learning-based Self-Adaptation of Human-Centered Internet of Things , year =

Hamed Rahimi and Iago Felipe Trentin and Fano Ramparany and Olivier Boissier , doi =. -SMASH: Q-Learning-based Self-Adaptation of Human-Centered Internet of Things , year =. ACM International Conference Proceeding Series , pages =
[199]

Epicurus at S em E val-2023 Task 4: Improving Prediction of Human Values behind Arguments by Leveraging Their Definitions

Fang, Christian and Fang, Qixiang and Nguyen, Dong. Epicurus at S em E val-2023 Task 4: Improving Prediction of Human Values behind Arguments by Leveraging Their Definitions. Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023). 2023. doi:10.186...

2023 doi
[200]

Frontiers in Computer Science , title =

Monique Hendriks and Martijn C Willemsen and Francesco Sartor and Jettie Hoonhout , doi =. Frontiers in Computer Science , title =
[201]

ACM International Conference Proceeding Series , title =

Albrecht Schmidt , doi =. ACM International Conference Proceeding Series , title =
[202]

Value stories: Putting human values into requirements engineering , volume =

Christian Detweiler and Maaike Harbers , editor =. Value stories: Putting human values into requirements engineering , volume =. CEUR workshop proceedings , publisher =
[203]

Learning alignment with human values: a case on route choice modeling via Inverse Reinforcement Learning , url =

Holgado S. Learning alignment with human values: a case on route choice modeling via Inverse Reinforcement Learning , url =. 2024 , school =

2024
[204]

Learning the value systems of agents with preference-based and inverse reinforcement learning , volume =

Andrés Holgado-Sánchez and Holger Billhardt and Alberto Fernández and Sascha Ossowski , doi =. Learning the value systems of agents with preference-based and inverse reinforcement learning , volume =. Autonomous Agents and Multi-Agent Systems , pages =
[205]

Value Learning for Value-Aligned Route Choice Modeling via Inverse Reinforcement Learning

Holgado-S \'a nchez, Andr \'e s and Bajo, Javier and Billhardt, Holger and Ossowski, Sascha and Arias, Joaqu \'i n. Value Learning for Value-Aligned Route Choice Modeling via Inverse Reinforcement Learning. Value Engineering in Artificial Intelligence. 2025

2025
[206]

Details withheld to preserve blind review

Authors. Details withheld to preserve blind review
[207]

2009 , issn =

Route choice modeling: past, present and future research directions , journal =. 2009 , issn =. doi:10.1016/S1755-5345(13)70005-8 , author =

2009 doi
[208]

Scheiner and C

J. Scheiner and C. Holz-Rau , doi =. Travel mode choice: Affected by objective or subjective determinants? , volume =. Transportation , pages =
[209]

Pre-proceedings of Value Engineering in AI (VALE track of the VECOMP 2024 Workshop, ECAI 2024) , pages =

Luis Oliva-Felipe, Inês Lobo, Jack McKinlay, Frank Dignum, Marina De Vos, Ulises Cortés and Atia Cortés , title =. Pre-proceedings of Value Engineering in AI (VALE track of the VECOMP 2024 Workshop, ECAI 2024) , pages =

2024
[210]

and Singh, Aarti and Dubrawski, Artur , title =

Xu, Yichong and Wang, Ruosong and Yang, Lin F. and Singh, Aarti and Dubrawski, Artur , title =. Proceedings of the 34th International Conference on Neural Information Processing Systems , articleno =. 2020 , isbn =

2020
[211]

A Survey of Preference-Based Reinforcement Learning Methods , journal =

Christian Wirth and Riad Akrour and Gerhard Neumann and Johannes F. A Survey of Preference-Based Reinforcement Learning Methods , journal =. 2017 , volume =

2017
[212]

Pre-proceedings of Value Engineering in AI (VALE track of the VECOMP 2024 Workshop, ECAI 2024) , pages =

Giovanni Dalmasso and Luis Marcos-Vidal and Clara Pretus , title =. Pre-proceedings of Value Engineering in AI (VALE track of the VECOMP 2024 Workshop, ECAI 2024) , pages =

2024
[213]

Aristoxenus at SemEval-2023 Task 4: A Domain-Adapted Ensemble Approach to the Identification of Human Values behind Arguments , year =

Dimitrios Zaikis and Stefanos D Stefanidis and Konstantinos Anagnostopoulos and Ioannis Vlahavas , editor =. Aristoxenus at SemEval-2023 Task 4: A Domain-Adapted Ensemble Approach to the Identification of Human Values behind Arguments , year =. 17th International Workshop on S...

2023
[214]

Empirical Software Engineering , title =

Arif Nurwidyantoro and Mojtaba Shahin and Michel Chaudron and Waqar Hussain and Harsha Perera and Rifat Ara Shams and Jon Whittle , doi =. Empirical Software Engineering , title =
[215]

2019 , archivePrefix=

Better-than-Demonstrator Imitation Learning via Automatically-Ranked Demonstrations , author=. 2019 , archivePrefix=

2019
[216]

Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems , pages =

Zeng, Zhiwei and Fan, Xiuyi and Miao, Chunyan and Leung, Cyril and Jih, Chin Jing and Soon, Ong Yew , title =. Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems , pages =. 2018 , publisher =

2018
[217]

Proceedings of the AAAI Conference on Artificial Intelligence , author=

Rating-Based Reinforcement Learning , volume=. Proceedings of the AAAI Conference on Artificial Intelligence , author=. 2024 , month=. doi:10.1609/aaai.v38i9.28886 , abstractNote=

2024 doi
[218]

Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations , author =. Proc. of the 36th Int. Conf. on Machine Learning , pages =. 2019 , volume =

2019
[219]

StFX-NLP at SemEval-2023 Task 4: Unsupervised and supervised approaches to detecting human values in arguments , year =

Ethan Heavey and Milton King and James Hughes , doi =. StFX-NLP at SemEval-2023 Task 4: Unsupervised and supervised approaches to detecting human values in arguments , year =. 17th International Workshop on Semantic Evaluation, SemEval 2023 - Proceedings of the Workshop , pages =

2023
[220]

Electronics (Switzerland) , title =

Eleanor Watson and Thiago Viana and Shujun Zhang and Benjamin Sturgeon and Lukas Petersson , doi =. Electronics (Switzerland) , title =
[221]

A Rose by Any Name? The Values Construct , volume =

Meg J Rohan , doi =. A Rose by Any Name? The Values Construct , volume =. Personality and Social Psychology Review , pages =
[222]

Advances in experimental social psychology , volume=

Universals in the content and structure of values: Theoretical advances and empirical tests in 20 countries , author=. Advances in experimental social psychology , volume=. 1992 , publisher=

1992
[223]

Schwartz value survey , author=
[224]

Online readings in Psychology and Culture , volume=

An overview of the Schwartz theory of basic values , author=. Online readings in Psychology and Culture , volume=
[225]

Schwartz and Gila Melech and Arielle Lehmann and Steven Burgess and Mari Harris and Vicki Owens , title =

Shalom H. Schwartz and Gila Melech and Arielle Lehmann and Steven Burgess and Mari Harris and Vicki Owens , title =. Journal of Cross-Cultural Psychology , volume =. 2001 , doi =. https://doi.org/10.1177/0022022101032005001 , abstract =

2001 doi
[226]

Multi-objective reinforcement learning for designing ethical multi-agent environments , year =

Manel Rodr. Multi-objective reinforcement learning for designing ethical multi-agent environments , year =. doi:10.1007/s00521-023-08898-y , journal =

doi
[227]

Akram Hossain and Abu Nowshed Chy , editor =

Abdul Aziz and Md. Akram Hossain and Abu Nowshed Chy , editor =. CSECU-DSG at SemEval-2023 Task 4: Fine-tuning DeBERTa Transformer Model with Cross-fold Training and Multi-sample Dropout for Human Values Identification , publisher=. 17th International Workshop on Semantic Eval...

2023
[228]

A Proposal for Selecting the Most Value-Aligned Preferences in Decision-Making Using Agreement Solutions , year =

Aar. A Proposal for Selecting the Most Value-Aligned Preferences in Decision-Making Using Agreement Solutions , year =. Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 1: EAA , publisher=. doi:10.5220/0012586300003636 , pages =

doi
[229]

Terry , journal =

Ralph Allan Bradley and Milton E. Terry , journal =. Rank Analysis of Incomplete Block Designs: I. The Method of Paired Comparisons , urldate =. 1952 , doi=

1952
[230]

Van Moffaert , K. and M.M. Drugan and A. Now \'e. Scalarized multi-objective reinforcement learning: novel design techniques. 2013 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL). 2013. doi:10.1109/ADPRL.2013.6615007

2013
[231]

Human Values Estimation on News Articles through BERT-extracted Opinion Expressions , publisher=

Yihong Han and Yoko Nishihara and Junjie Shan , doi =. Human Values Estimation on News Articles through BERT-extracted Opinion Expressions , publisher=. Proceedings - 2022 International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2022 , pages =

2022
[232]

Value-Awareness Engineering: Towards Learning Context-Based Value Taxonomies , volume =

Andr. Value-Awareness Engineering: Towards Learning Context-Based Value Taxonomies , volume =. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , pages =. doi:10.1007/978-3-031-43264-4\_35 , editor =

doi
[233]

Entropy , title =

Amir Reza Jafari and Praboda Rajapaksha and Reza Farahbakhsh and Guanlin Li and Noel Crespi , doi =. Entropy , title =
[234]

Active Reward Learning from Multiple Teachers , volume =

Peter Barnett and Rachel Freedman and Justin Svegliato and Stuart Russell , editor =. Active Reward Learning from Multiple Teachers , volume =. CEUR workshop proceedings , publisher =
[235]

SCaLAR NITK at Touché: Comparative Analysis of Machine Learning Models for Human Value Identification , volume =

K Praveen and R K Darshan and Chinta Tejdeep Reddy and M Anand Kumar , editor =. SCaLAR NITK at Touché: Comparative Analysis of Machine Learning Models for Human Value Identification , volume =. CEUR workshop proceedings , publisher =
[236]

Learning to identify and settle dilemmas through contextual user preferences , publisher =

Remy Chaput and Laetitia Matignon and Mathieu Guillermin , doi =. Learning to identify and settle dilemmas through contextual user preferences , publisher =. Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI , pages =
[237]

Artificial intelligence and learning environment: Human considerations , volume =

Esmaeil Jafari , doi =. Artificial intelligence and learning environment: Human considerations , volume =. Journal of Computer Assisted Learning , pages =
[238]

Value FULCRA: Mapping Large Language Models to the Multidimensional Spectrum of Basic Human Values , volume =

Jing Yao and Xiaoyuan Yi and Yifan Gong and Xiting Wang and Xing Xie , editor =. Value FULCRA: Mapping Large Language Models to the Multidimensional Spectrum of Basic Human Values , volume =. Proceedings of the 2024 Conference of the North American Chapter of the Association f...

2024
[239]

2019 , publisher=

Human compatible: AI and the problem of control , author=. 2019 , publisher=

2019
[240]

Learning What to Value , year =

Daniel Dewey , address =. Learning What to Value , year =. Artificial General Intelligence , pages =
[241]

Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems , pages =

Osman, Nardine and d'Inverno, Mark , title =. Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems , pages =. 2024 , isbn =

2024
[242]

On the relevance of value system structure for automated value-aligned decision-making , year =

Marcelo Karanik and Holger Billhardt and Alberto Fernández and Sascha Ossowski , doi =. On the relevance of value system structure for automated value-aligned decision-making , year =. Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing , keywords =
[243]

, title =

Kettenring, Jon R. , title =. Journal of Classification , year =. doi:10.1007/s00357-006-0002-6 , issn =

doi
[244]

A Qualitative Approach to Composing Value-Aligned Norm Systems , year =

Marc Serramia and Maite L. A Qualitative Approach to Composing Value-Aligned Norm Systems , year =. Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems , keywords =
[245]

van der Weide, T. L. and Dignum, F. and Meyer, J. -J. Ch. and Prakken, H. and Vreeswijk, G. A. W. Practical Reasoning Using Values. Argumentation in Multi-Agent Systems. 2010

2010
[246]

2024 , doi=

A Survey of Reinforcement Learning from Human Feedback , author=. 2024 , doi=. 2312.14925 , archivePrefix=

2024
[247]

and Borning, Alan and Huldtgren, Alina

Friedman, Batya and Kahn, Peter H. and Borning, Alan and Huldtgren, Alina. Value Sensitive Design and Information Systems. Early engagement and new technologies: Opening up the laboratory. 2013. doi:10.1007/978-94-007-7844-3\_4

2013 doi
[248]

Autonomous Agents and Multi-Agent Systems , volume=

Using argumentation to model agent decision making in economic experiments , author=. Autonomous Agents and Multi-Agent Systems , volume=. 2012 , doi=

2012
[249]

2018 , publisher=

Reinforcement learning: An introduction , author=. 2018 , publisher=

2018
[250]

Proceedings of the 33rd International Conference on Neural Information Processing Systems , articleno =

Jiang, Jiechuan and Lu, Zongqing , title =. Proceedings of the 33rd International Conference on Neural Information Processing Systems , articleno =. 2019 , publisher =

2019
[251]

Proceedings of the AAAI Conference on Artificial Intelligence , author=

Proportional Aggregation of Preferences for Sequential Decision Making , volume=. Proceedings of the AAAI Conference on Artificial Intelligence , author=. 2024 , month=. doi:10.1609/aaai.v38i9.28813 , abstractNote=

2024 doi
[252]

Journal of Artificial Intelligence Research , volume=

Synthesis and properties of optimally value-aligned normative systems , author=. Journal of Artificial Intelligence Research , volume=. 2022 , doi=

2022
[253]

Encoding Ethics to Compute Value-Aligned Norms , journal =

Serramia, Marc and Rodr. Encoding Ethics to Compute Value-Aligned Norms , journal =. 2023 , month =

2023
[254]

2025 , eprint=

Learning to Extract Context for Context-Aware LLM Inference , author=. 2025 , eprint=

2025
[255]

and Finn, Chelsea , title =

Rafailov, Rafael and Sharma, Archit and Mitchell, Eric and Ermon, Stefano and Manning, Christopher D. and Finn, Chelsea , title =. Proceedings of the 37th International Conference on Neural Information Processing Systems , articleno =. 2023 , publisher =

2023
[256]

Towards pluralistic value alignment: Aggregating value systems through lp-regression , volume =

R Lera-Leri and F Bistaffa and M Serramia and M L. Towards pluralistic value alignment: Aggregating value systems through lp-regression , volume =. openaccess.city.ac.ukR Lera-Leri, F Bistaffa, M Serramia, M L
[257]

An approach to computing ethics , volume =

Michael Anderson and Susan Leigh Anderson and Chris Armen , doi =. An approach to computing ethics , volume =. IEEE Intelligent Systems , publisher=
[258]

ETHEL: Toward a Principled Ethical Eldercare System

Michael Anderson and Susan Leigh Anderson , journal =. ETHEL: Toward a Principled Ethical Eldercare System. , volume =
[259]

GenEth: A general ethical dilemma analyzer , volume =

Michael Anderson and Susan Leigh Anderson , doi =. GenEth: A general ethical dilemma analyzer , volume =. Paladyn , keywords =
[260]

2009 , publisher=

Ethics for the information age , author=. 2009 , publisher=

2009
[261]

and Nichols, Jeffrey , title =

Chen, Jilin and Hsieh, Gary and Mahmud, Jalal U. and Nichols, Jeffrey , title =. Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work & Social Computing , pages =. 2014 , isbn =. doi:10.1145/2531602.2531608 , abstract =

2014
[262]

Value Inference in Sociotechnical Systems , year =

Enrico Liscio and Roger Lera-Leri and Filippo Bistaffa and Roel IJ Dobbe and Catholijn M Jonker and Maite L. Value Inference in Sociotechnical Systems , year =. Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems , pages =

2023
[263]

Multi-objective reinforcement learning for guaranteeing alignment with multiple values , year =

Manel Rodr. Multi-objective reinforcement learning for guaranteeing alignment with multiple values , year =. 2023 Adaptive and Learning Agents Workshop at AAMAS , month =

2023
[264]

2026 , issn =

Multi-objective reinforcement learning for provably incentivising alignment with value systems , journal =. 2026 , issn =. doi:10.1016/j.artint.2025.104460 , author =

2026
[265]

A Virtue-Based Framework to Support Putting AI Ethics into Practice , volume =

Thilo Hagendorff , doi =. A Virtue-Based Framework to Support Putting AI Ethics into Practice , volume =. Philosophy and Technology , keywords =
[266]

Bauer , doi =

William A. Bauer , doi =. Virtuous vs. utilitarian artificial moral agents , volume =. AI and Society , keywords =
[267]

2025 , eprint=

The Augmented Lagrangian Methods: Overview and Recent Advances , author=. 2025 , eprint=

2025
[268]

Proceedings of the 35th International Conference on Neural Information Processing Systems , articleno =

Sato, Ryo and Tanaka, Mirai and Takeda, Akiko , title =. Proceedings of the 35th International Conference on Neural Information Processing Systems , articleno =. 2021 , isbn =

2021
[269]

A framework for bilevel optimization that enables stochastic and global variance reduction algorithms , year =

Dagr\'. A framework for bilevel optimization that enables stochastic and global variance reduction algorithms , year =. Proceedings of the 36th International Conference on Neural Information Processing Systems , articleno =
[270]

Learning the Value Systems of Societies from Preferences , booktitle =

Andr. Learning the Value Systems of Societies from Preferences , booktitle =. 2025 , doi =

2025
[271]

Sorensen, Taylor and Jiang, Liwei and Hwang, Jena D. and Levine, Sydney and Pyatkin, Valentina and West, Peter and Dziri, Nouha and Lu, Ximing and Rao, Kavel and Bhagavatula, Chandra and Sap, Maarten and Tasioulas, John and Choi, Yejin , title =. Proceedings of the Thirty-Eigh...

2024
[272]

Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society , author=

Aggregation Problems in Machine Ethics and AI Alignment , volume=. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society , author=. 2025 , month=. doi:10.1609/aies.v8i1.36554 , abstractNote=

2025 doi
[273]

Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society , author=

ValuesRAG: Enhancing Cultural Alignment Through Retrieval-Augmented Contextual Learning , volume=. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society , author=. 2025 , month=. doi:10.1609/aies.v8i3.36717 , abstractNote=

2025 doi
[274]

Learning the Value Systems of Societies from Preferences , booktitle =

Eduardo de la Cruz Fern. Learning the Value Systems of Societies from Preferences , booktitle =. 2025 , doi =

2025
[275]

2022 , eprint=

Constitutional AI: Harmlessness from AI Feedback , author=. 2022 , eprint=

2022
[276]

Self-supervised alignment with mutual information: learning to follow principles without preference labels , year =

Fr\". Self-supervised alignment with mutual information: learning to follow principles without preference labels , year =. Proceedings of the 38th International Conference on Neural Information Processing Systems , articleno =
[277]

PERSONA : A Reproducible Testbed for Pluralistic Alignment

Castricato, Louis and Lile, Nathan and Rafailov, Rafael and Fr. PERSONA : A Reproducible Testbed for Pluralistic Alignment. Proceedings of the 31st International Conference on Computational Linguistics. 2025

2025
[278]

2022 , publisher =

Haerpfer, Christian and Inglehart, Ronald and Moreno, Alejandro and Welzel, Christian and Kizilova, Kseniya and Diez-Medrano, Jaime and Lagos, Milena and Norris, Pippa and Ponarin, Eduard and Puranen, Bianca , title =. 2022 , publisher =

2022
[279]

Personal

Thomas P Zollo and Andrew Wei Tung Siah and Naimeng Ye and Ang Li and Hongseok Namkoong , booktitle=. Personal. 2025 , url=

2025
[280]

Proceedings of the 38th International Conference on Neural Information Processing Systems , articleno =

Gao, Ge and Taymanov, Alexey and Salinas, Eduardo and Mineiro, Paul and Misra, Dipendra , title =. Proceedings of the 38th International Conference on Neural Information Processing Systems , articleno =. 2024 , isbn =

2024
[281]

The Twelfth International Conference on Learning Representations , year=

Group Preference Optimization: Few-Shot Alignment of Large Language Models , author=. The Twelfth International Conference on Learning Representations , year=
[282]

Ignacio D. L. OFTEN-DeepRL: On-the-Fly Teaching of Ethical Norms to Deep Reinforcement Learning Agents , booktitle =. 2025 , doi =

2025
[283]

Joint Goal and Strategy Inference across Heterogeneous Demonstrators via Reward Network Distillation , year =

Letian Chen and Rohan Paleja and Muyleng Ghuy and Matthew Gombolay , city =. Joint Goal and Strategy Inference across Heterogeneous Demonstrators via Reward Network Distillation , year =. Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction , ke...

2020
[284]

2024 , MONTH = 6, KEYWORDS =

Holgado-S. 2024 , MONTH = 6, KEYWORDS =

2024
[285]

, title =

Vrtic, Milenko and Axhausen, Kay W. , title =. 2002 , note =

2002
[286]

J. G. Kemeny and L. J. Snell , title =. Mathematical Models in the Social Sciences , pages =
[287]

Towards Value Awareness in the Medical Field , booktitle=

Manel Rodr. Towards Value Awareness in the Medical Field , booktitle=. 2024 , pages=. doi:10.5220/0012588600003636 , isbn=

2024 doi
[288]

Principles of Clinical Ethics and Their Application to Practice

Varkey, Basil. Principles of Clinical Ethics and Their Application to Practice. Med Princ Pract
[289]

2008 , publisher=

Moral machines: Teaching robots right from wrong , author=. 2008 , publisher=

2008
[290]

2025 , booktitle =

Abia Alonso, Víctor and Serramia, Marc and Alonso, Eduardo , title =. 2025 , booktitle =

2025
[291]

Proceedings of the 11th Biennial Participatory Design Conference , pages =

Iversen, Ole Sejer and Halskov, Kim and Leong, Tuck Wah , title =. Proceedings of the 11th Biennial Participatory Design Conference , pages =. 2010 , isbn =. doi:10.1145/1900441.1900455 , abstract =

2010
[292]

MoralStrength: Exploiting a moral lexicon and embedding similarity for moral foundations prediction , volume =

Oscar Araque and Lorenzo Gatti and Kyriaki Kalimeri , doi =. MoralStrength: Exploiting a moral lexicon and embedding similarity for moral foundations prediction , volume =. Knowledge-Based Systems , keywords =
[293]

Using soft maximin for risk averse multi-objective decision-making , volume =

Benjamin J Smith and Robert Klassert and Roland Pihlakas , doi =. Using soft maximin for risk averse multi-objective decision-making , volume =. Autonomous Agents and Multi-Agent Systems , pages =
[294]

, title =

Neufeld, Emery A. , title =. 2022 , booktitle =. doi:10.5220/0010835600003116 , url =

2022 doi
[295]

, title =

Neufeld, Emery A. , title =. 2024 , journal =. doi:10.1007/s13218-024-00844-x , affiliations =

2024 doi
[296]

Artificial Moral Agents: A Survey of the Current Status , volume =

José-Antonio Cervantes and Sonia López and Luis-Felipe Rodríguez and Salvador Cervantes and Francisco Cervantes and Félix Ramos , doi =. Artificial Moral Agents: A Survey of the Current Status , volume =. Science and Engineering Ethics , pages =
[297]

2024 , volume =

Chakraborty, Souradip and Qiu, Jiahao and Yuan, Hui and Koppel, Alec and Manocha, Dinesh and Huang, Furong and Bedi, Amrit and Wang, Mengdi , booktitle =. 2024 , volume =

2024
[298]

doi:10.25338/B81S5M , url =

Amenta, Annamaria and Sanguinetti, Angela , title =. doi:10.25338/B81S5M , url =

doi
[299]

science , volume=

The new synthesis in moral psychology , author=. science , volume=. 2007 , publisher=

2007
[300]

Philosophical Studies , pages=

Beyond preferences in AI alignment , author=. Philosophical Studies , pages=. 2024 , publisher=

2024

Pith tools

Reviewed August 1, 2026 · model on record in the stance chip above.

[1] [1]

The 9th International Workshop on Freight Transportation and Logistics (ODYSSEUS) , title =

L. The 9th International Workshop on Freight Transportation and Logistics (ODYSSEUS) , title =

[2] [2]

XIII International Workshop on Locational Analysis and Related Problems (IWOLOCA) , title =

L. XIII International Workshop on Locational Analysis and Related Problems (IWOLOCA) , title =

[3] [3]

Vehicle Routing Problem with Fair Profits and Time Windows (

L. Vehicle Routing Problem with Fair Profits and Time Windows (. 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC) , pages=. 2023 , organization=

2023

[4] [4]

CEUR Proceedings, 12th Int

On balancing fairness and efficiency in routing of cooperative vehicle fleets , author=. CEUR Proceedings, 12th Int. Workshop on ATT 2022 co-located with IJCAI-ECAI 2022 , volume=

2022

[5] [5]

2024 , journal =

How to achieve fair and efficient cooperative vehicle routing? , author =. 2024 , journal =

2024

[6] [6]

2024 , month = oct, journal =

Dynamic, Fair, and Efficient Routing for Cooperative Autonomous Vehicle Fleets , author =. 2024 , month = oct, journal =

2024

[7] [7]

Multi-Objective Deep Inverse Reinforcement Learning through Direct Weights and Rewards Estimation , year=

Kishikawa, Daiko and Arai, Sachiyo , booktitle=. Multi-Objective Deep Inverse Reinforcement Learning through Direct Weights and Rewards Estimation , year=

[8] [8]

Comparison of distance and reinforcement-learning rules in social-influence models , volume =

Norberto M Grzywacz , doi =. Comparison of distance and reinforcement-learning rules in social-influence models , volume =. Neurocomputing , keywords =