Pith. sign in

REVIEW 4 major objections 5 minor 7 cited by

Simulating Human-like Daily Activities with Desire-driven Autonomy

T0 review · 4 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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

Pith's one-line read An agent can behave like a person by chasing desires instead of following tasks.

desk verdict A solid integration of desire-driven autonomy for LLM agents with a real behavioral win-rate result, but the mechanistic claim rests on a self-referential metric that needs independent calibration. read the letter →

arxiv 2412.06435 v3 pith:MZJFWOLA submitted 2024-12-09 cs.AI

Yiding Wang , Yuxuan Chen , Fangwei Zhong , Long Ma , Yizhou Wang This is my paper · ORCID
classification cs.AI
keywords desire-drivenautonomylargelanguagemodelsLLMagentshuman-likeactivitysimulationTheoryofNeedsvaluesystemintrinsicmotivationtext-based
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

The paper tries to show that an LLM can act human-like without explicit tasks if it is driven by internal desires. It introduces D2A, an agent whose value system tracks desire dimensions such as hunger, health, social connectivity, joy, and spiritual satisfaction; at each step the agent proposes candidate activities, imagines how each would change its desires, and selects the one that best closes the gap to its expected values. In text-based indoor and outdoor simulations, D2A's activity sequences were judged more natural, coherent, and plausible than those of ReAct, BabyAGI, and LLMob, and its desire-satisfaction trajectory came closest to human-generated routines. If this holds, intrinsic motivational state—not task instructions or reward functions—could be the right driver for autonomous, human-like agent behavior.

What carries the argument

The mechanism is the Value System and the Desire-driven Planner working in a loop. The Value System keeps a numeric score for each desire dimension, decays scores probabilistically according to the profile, translates numbers into qualitative descriptions, and updates scores after each action using the LLM. The Planner proposes N candidate activities, imagines the post-action desire state of each, and selects the one with the most positive predicted impact. The reported Dissatisfaction metric—the sum over desires of the positive gap between expected and current values—is what ties the mechanism to the human-likeness evaluation.

What would settle it

Have independent human raters re-score the post-action desire values from the same transcripts, then recompute the dissatisfaction curves; if D2A's advantage over ReAct shrinks or reverses, the claimed human-likeness gain rests on the agent grading its own homework.

Watch

Extended reading notes

Core claim

The central discovery is that a desire-driven loop is enough to produce human-like daily activity. D2A beats ReAct (0.85 win rate), LLMob (0.72), and BabyAGI (0.66) in GPT-4o judged human-likeness, and in the Fixed-12 experiment its average dissatisfaction curve is the closest automated match to three human annotators. The paper attributes this to desire-based motivation: D2A continuously evaluates its current desire deficits, proposes several candidate activities, predicts their effects, and chooses the one that best reduces the deficit, which yields sequences that are natural, coherent, and plausible.

Load-bearing premise

The desire-satisfaction results assume the same language model that chooses each activity also assigns accurate numerical updates to its own desire states after the activity.

Editorial extensions

If this is right

  • An LLM agent can sustain long-running behavior from internal desire states alone, without task instructions or reward functions, while staying coherent and contextually appropriate.
  • The dissatisfaction metric gives a quantitative handle on human-likeness: D2A's curves most closely match human-generated routines, so desire-satisfaction can be measured and optimized directly.
  • The ablations show qualitative desire descriptions and a wider candidate-activity search matter, while multi-step planning hurts, which suggests immediate desire feedback is the operative mechanism.
  • The framework transfers to social, multi-agent outdoor settings and to a different backbone LLM, so the mechanism is not tied to one environment or one model.

Reading between the lines

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

  • The paper does not test this, but the same architecture could be read as an intrinsic-reward model: the desire deficit is a learnable signal that could steer behavior without external reward functions.
  • A testable extension would hold the environment fixed and vary only the desire dimensions and decay rates; the framework predicts that action diversity and category balance track the set of unmet desires rather than the LLM's prior knowledge.
  • Since the agent both chooses activities and assigns its own desire updates, independent re-scoring of value changes would separate genuine behavioral improvement from self-consistent scoring.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper introduces D2A, a desire-driven autonomous agent built on a multi-dimensional Value System inspired by the Theory of Needs, which enables an LLM to propose, evaluate, and select daily activities without explicit task instructions. The agent is evaluated in a Concordia-based text environment (indoor and outdoor, single- and multi-agent) against ReAct, BabyAGI, and LLMob. Using GPT-4o pairwise comparisons and a small human consistency check, the authors report that D2A generates activity sequences that are more natural, coherent, and plausible than the baselines; using a 'dissatisfaction' metric, they further claim that D2A satisfies its desires similarly to humans.

Significance. If the main claims hold, D2A is a meaningful step toward LLM agents with intrinsic motivation, with potential applications in simulation, games, and service agents. The paper has several concrete strengths: the framework is described in sufficient detail to reimplement from the appendix prompts; the evaluation spans two environments with different desire sets and demonstrates some generalization (including a different backbone in Appendix P); and the GPT-4o judgments are partially grounded by a human consistency study (Appendix B). However, the load-bearing evidence for the desire-satisfaction mechanism is weakened by flaws in the dissatisfaction metric and by the self-referential nature of the value update, and the behavioral win-rate evidence lacks statistical grounding and code release.

major comments (4)
  1. [Section 6.2, Eq. (6), Appendix H] The dissatisfaction metric in Eq. (6) has the wrong sign for the physiological dimensions. The value descriptions in Appendix H define hunger, thirst, and sleepiness such that 0 is the satisfied state and 10 is the deprived state, yet Eq. (6) uses max(v*_d - v_t_d, 0) for all dimensions, which yields zero dissatisfaction for a high (deprived) value of hunger relative to its expected value (e.g., hunger=8 vs. v*=2). This contradicts the case study in Appendix C.2, where hunger=8 is treated as a pressing need. The metric should be max(v_t_d - v*_d, 0) for these three dimensions (or, equivalently, the value scales should be inverted before applying Eq. 6). Because the metric is used to compare D2A with baselines in Figures 4(a), 4(b), 5, and 16, the quantitative support for the desire-satisfaction claim is questionable.
  2. [Sections 5.2 and 5.3, Eq. (6)] The Dissatisfaction curves in Section 6.3.2 are computed from value updates produced by the same Llama3.1-70B model that, in the Activity Evaluation (Eq. 4) and Activity Selection (Eq. 5) procedures, already imagines the desire states of candidate actions and selects the action that best fits that imagination. When this value-update model is applied to the baselines' actions, its ratings may be systematically more favorable for D2A's self-chosen actions, making the lower Dissatisfaction of D2A at least partly an artifact of model self-consistency rather than a demonstration of human-like need regulation. The human consistency check in Appendix B validates only the GPT-4o human-likeness judge, not the Value Update. The authors should calibrate the value update against human ratings or an independent, fixed reward rule, or explicitly argue that self-consistency cannot produce the observed gap.
  3. [Section 6.3.1, Figure 3, Appendix B] The pairwise win rates in Figure 3 are reported without confidence intervals or significance tests, so the repeated use of 'significantly' (e.g., in Section 6.3.2) is not supported by the displayed statistics. The human consistency study in Appendix B is based on 19 pairs and 8 annotators per questionnaire; the consistency rate for the 'difficult to distinguish' subset is 66.7%, and no inter-annotator agreement measure (e.g., Cohen's kappa) is reported. The authors should add binomial confidence intervals or a permutation test for the win rates and report inter-annotator agreement to support the claim that GPT-4o evaluations are reliable.
  4. [Section 3 and Section 6.3.1 (style rewriting); reproducibility] The activity sequences are style-rewritten with Llama3.1-70B before evaluation, but no analysis is provided that the rewrite preserves the content of the original sequences; since the raw sequences in Appendix V differ substantially across agents in granularity and detail, the rewriting could change the comparability. In addition, the paper does not release code, prompts, or data, which is particularly problematic because the core claims depend on the exact prompts and the value update procedure described in Appendices I-K. The authors should release the code and prompts (or a detailed pseudocode) and provide at least a small human check of the rewritten sequences against the originals.
minor comments (5)
  1. [Section 6.3.1] Section 6.3.1 refers to 'Appendix 1'; this should be Appendix B.
  2. [Section 6.1] In Section 6.1 a 'human-controlled agent' is mentioned but is not described in the experimental comparisons until Section 6.3.2; clarify its role in the setup.
  3. [Table 8, Appendix G] The expected value formulas in Table 8 (e.g., '3 − Degree') can yield non-integer values even though Appendix G states that all numerical values are integers; specify how these values are rounded or discretized in the implementation.
  4. [Appendix D] The heading 'D CASESTUDY FOR INOUTDOORENVIRONMENT' (Appendix D) contains missing spaces, and the section labels 'D.1' and 'D.2' are not consistently formatted; the paper would benefit from a careful proofreading pass.
  5. [Abstract] The abstract states that D2A 'significantly enhances the rationality' of simulated activities, but the evaluation criteria measured are naturalness, coherence, and plausibility; the term 'rationality' should be either defined or replaced by the measured criteria.

Circularity Check

2 steps flagged · score 5.0 of 10

GPT-4o win-rate evidence is externally judged, but the desire-satisfaction metric (Eq. 6) is self-referential: D2A selects actions using the same LLM-based value system that later scores them, so Figure 4 partly measures self-consistency.

  1. self definitional [Section 5.2-5.3, Eq. (6), Appendix J]
    "Afterward, the Value System initiates the Value Update procedure, updating the intrinsic numerical values v t of the desire components based on the activity a t, the observation o t, the previous values v t−1, and the value description prompts (Appendix H). ... Dissatisfaction δ t of step t is defined as the relative difference between current numerical desire values v t and expected desire values v ∗ ... δt = X d max(v∗ d − v t d,0) (6)"

    The Value Update that produces v^t is executed by the same Llama3.1-70B model that D2A uses in Activity Proposal (Eq. 3), Activity Evaluation (Eq. 4), and Activity Selection (Eq. 5). D2A therefore chooses a_t to maximize the LLM's imagined desire states, and the same LLM then writes the post-hoc v^t values. Eq. 6 is the agent's own objective scored by its own reward model; Appendix J even calls the updater 'the reward model.' No independent calibration of these updates against human desire ratings is provided—Appendix B only validates the GPT-4o human-likeness judge. Hence Figure 4 cannot separate genuine need regulation from LLM self-consistency.

  2. other [Section 6.3.2, Random-8-Steps Experiment]
    "Although the baseline agents were equipped with the Value System for observation, we ensured that their intrinsic values v and descriptive desire states D remained hidden, preserving their original activity generation mechanisms."

    D2A is given D_t and v* and is explicitly prompted to 'generate K activities that might have most positive impact on your own physical desires or value states' and to 'choose the action that has the most positive impact,' while baselines are deliberately denied that information. The Random-8 comparison then reports δ_t from Eq. 6 for both types of agents. This compares an optimizer with full access to the evaluation objective against policies that are blind to it; the lower D2A dissatisfaction is largely a consequence of the experimental design, not independent evidence for human-like desire regulation.

full rationale

The main human-likeness claim rests on GPT-4o pairwise win rates (Figure 3), which are validated against human annotators in Appendix B; that evaluation is external to the agent and not circular. The circularity is confined to Section 6.3.2's dissatisfaction experiments. D2A's Planner proposes, evaluates, and selects activities using imagined desire states produced by Llama3.1-70B (Eqs. 3-5), and the same backbone's Value Update then assigns the numerical v^t used in Eq. 6. The agent is thus scored by the same reward model it optimizes; the Random-8 protocol even denies the baselines access to D_t and v while D2A is given them. Without any calibration of the Value Update against human desire ratings (Appendix B validates only the GPT-4o judge), the lower D2A dissatisfaction is expected by construction and does not independently establish human-like need regulation. No load-bearing self-citation or uniqueness-theorem argument appears; the likelihood comparison remains a genuine behavioral result.

Assumptions & free parameters 5 free parameters · 6 assumptions · 1 invented entities

The framework's behavior depends on a set of hand-chosen constants (expected desire values, decay probabilities, initial values, planner width) and on the assumption that the LLM can correctly assess desire changes. The desire value system is an internal construct without external validation, so the main empirical claims rest partly on self-referential measurements.

free parameters (5)
  • Expected desire values v*_d = e.g., 9 for extremely sociable, 7 for slightly sociable; formulas in Table 8
    Set by the hand-crafted adjective-degree map (Eq. 2, Tables 7-8). These targets define what counts as a satisfied desire and therefore shape all dissatisfaction scores.
  • Desire decay probability p_d = Degree / (60 / step_interval), with Degree in {0.5, 1.0, 1.5, 2.0}
    Controls how quickly each need depletes each step; chosen heuristically, not calibrated to human data (Appendix G).
  • Initial desire values v0_d = uniform random in [0,10]
    Random initialization for desire generation (Section 5.1); affects the transient but the paper argues results are averaged over trials.
  • Planner width N = 3 (ablated to 1 and 5)
    Number of candidate activities proposed each step; shown to affect performance (Section 6.4).
  • Adjective-degree pairings = random pairing at initialization
    Each trait is randomly paired with an adverb of degree, affecting expected values; the mapping is arbitrary (Section 5.1).
assumptions (6)
  • domain assumption Maslow's Theory of Needs correctly describes human motivation as the drive to satisfy intrinsic needs.
    Adopted in Section 1 and 5.1 to justify the desire dimensions; a psychological theory, not a proven mechanism.
  • ad hoc to paper Personality adjectives and degree adverbs map to expected desire values via the predefined map (Eq. 2 and Tables 7-8).
    The mapping values (e.g., 'extremely' maps to 2.0) are chosen by the authors and are not derived from data.
  • ad hoc to paper The chosen 11 (indoor) and 14 (outdoor) desire dimensions are sufficient to capture human-like motivation in these scenarios.
    The authors acknowledge this is heuristic and environment-dependent (Limitations, Section 7).
  • domain assumption Language models can reliably translate numeric desire values into qualitative descriptions and back, and can update values after actions.
    The Value Update procedure asks the LLM to judge how each action changes each desire magnitude (Appendix J); no ground-truth validation.
  • ad hoc to paper Style rewriting with Llama3.1-70B preserves the semantic content of activity sequences.
    Section 6.3.1 applies rewriting before GPT-4o evaluation; the paper does not verify that content is preserved.
  • domain assumption GPT-4o's human-likeness judgments agree with human judgments.
    Supported only by the small consistency check in Appendix B (19 pairs).
invented entities (1)
  • Multi-dimensional Value System (desire components)
    purpose: Quantify and update the agent's internal need states; drives activity proposal, evaluation, and selection.
    The desire values are initialized from random numbers, updated by LLM judgments, and never compared to external measurements of human needs. The 'satisfaction' it measures is internally defined.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Simulating Human-like Daily Activities with Desire-driven Autonomy." pith.science (2026). https://pith.science/paper/MZJFWOLA

@misc{pith2026241206435,
  author       = {Pith},
  title        = {Pith review of: Simulating Human-like Daily Activities with Desire-driven Autonomy},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MZJFWOLA}},
  note         = {Machine review of arXiv:2412.06435}
}
read the original abstract

Desires motivate humans to interact autonomously with the complex world. In contrast, current AI agents require explicit task specifications, such as instructions or reward functions, which constrain their autonomy and behavioral diversity. In this paper, we introduce a Desire-driven Autonomous Agent (D2A) that can enable a large language model (LLM) to autonomously propose and select tasks, motivated by satisfying its multi-dimensional desires. Specifically, the motivational framework of D2A is mainly constructed by a dynamic Value System, inspired by the Theory of Needs. It incorporates an understanding of human-like desires, such as the need for social interaction, personal fulfillment, and self-care. At each step, the agent evaluates the value of its current state, proposes a set of candidate activities, and selects the one that best aligns with its intrinsic motivations. We conduct experiments on Concordia, a text-based simulator, to demonstrate that our agent generates coherent, contextually relevant daily activities while exhibiting variability and adaptability similar to human behavior. A comparative analysis with other LLM-based agents demonstrates that our approach significantly enhances the rationality of the simulated activities.

Figures

Figures reproduced from arXiv: 2412.06435 by the authors.

Figure 1
Figure 1. Motivation Behind Different Activity Generation Approaches. Our Desire-driven Au [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. The Desire-Driven Autonomy framework. The [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. The win rate among 4 agents. Each point represents the win rate of the agent on the vertical axis when compared with the agent on the horizontal axis. The diagonal values are set to 0.5. The results in [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (13 more)
Figure 4
Figure 4. Figure 4: The results of D2A human-likeness analysis experiments. [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]
Figure 5
Figure 5. Figure 5: The averaged dissatisfaction re￾sults in the outdoor environment To assess the adaptability of our D2A framework within complex social interaction scenarios, we conducted ex￾periments in the outdoor environment. The results, pre￾sented in [PITH_FULL_IMAGE:figures/full…
Figure 6
Figure 6. Figure 6: Ablation study regarding (a) Qualitative Value Description, (b) Searching Width of the [PITH_FULL_IMAGE:figures/full_fig_p010_6.png]
Figure 7
Figure 7. Figure 7: The change in each dissatisfaction dimension at every step within the outdoor environment [PITH_FULL_IMAGE:figures/full_fig_p018_7.png]
Figure 8
Figure 8. Figure 8: The changes in desire at each step within the outdoor environment are illustrated; specifi [PITH_FULL_IMAGE:figures/full_fig_p018_8.png]
Figure 9
Figure 9. Figure 9: The change in each dissatisfaction dimension at every step within the indoor environment [PITH_FULL_IMAGE:figures/full_fig_p019_9.png]
Figure 10
Figure 10. Figure 10: The changes in desire at each step within the indoor environment are illustrated; the [PITH_FULL_IMAGE:figures/full_fig_p020_10.png]
Figure 11
Figure 11. Figure 11: The word clouds of the four agents are based on the same environment and settings in [PITH_FULL_IMAGE:figures/full_fig_p021_11.png]
Figure 12
Figure 12. Figure 12: We classified each action output by all agents at every step, allowing each action to belong [PITH_FULL_IMAGE:figures/full_fig_p021_12.png]
Figure 13
Figure 13. Figure 13: The win rates among 4 agents in outdoor environments. Each point represents the win [PITH_FULL_IMAGE:figures/full_fig_p022_13.png]
Figure 14
Figure 14. Figure 14: The word clouds of the four agents are based on the same environment and settings in [PITH_FULL_IMAGE:figures/full_fig_p023_14.png]
Figure 15
Figure 15. Figure 15: The experiments conducted with Qwen2.5:72b model [PITH_FULL_IMAGE:figures/full_fig_p031_15.png]
Figure 16
Figure 16. Figure 16: In randomly initialized indoor environments, D2A’s action sequences also best addressed [PITH_FULL_IMAGE:figures/full_fig_p046_16.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 7 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Step-Level Preference Learning for Generative Agents in Social Simulations

    cs.AI 2026-07 conditional novelty 7.0 of 10

    Step-level human preference data collected via SimPref, then SFT+DPO, improves long-horizon social-simulation behavior of open-weight LLM agents on held-out events.

  2. CAMS: A CityGPT-Powered Agentic Framework for Urban Human Mobility Simulation

    cs.CL 2025-06 conditional novelty 6.0 of 10

    CAMS, an agentic framework built on CityGPT, generates urban mobility trajectories from user profiles and claims superior JSD-based performance over prior simulators.

  3. IndoorWorld: Integrating Physical Task Solving and Social Simulation in A Heterogeneous Multi-Agent Environment

    cs.MA 2025-06 conditional novelty 6.0 of 10

    IndoorWorld is a new multi-agent environment that combines physical task solving with social interaction, and its experiments show effects of collaboration, resource competition, and layout on agent behavior.

  4. Matryoshka Agent: Unfolding Sub-Agents for Long-Horizon Machine Learning Engineering

    cs.AI 2026-07 conditional novelty 5.0 of 10

    Matryoshka Agent’s orchestrator–sub-agent hierarchy plus tree-ranked RL raises MLE-Dojo HumanRank, letting a 4B orchestrator approach o4-mini and giving a 30B coder up to 36.7% relative gain.

  5. Simulating Human Behavior with the Psychological-mechanism Agent: Integrating Feeling, Thought, and Action

    cs.HC 2025-06 reject novelty 5.0 of 10

    PSYA combines ALMA emotion layers and the Triple Network Model to make LLM agents behave more human-like and reproduce several classic psychology experiment results.

  6. Toward LLM-Agent-Based Modeling of Transportation Systems: A Conceptual Framework

    cs.AI 2024-12 conditional novelty 5.0 of 10

    LLM-driven agents with profiles, memory, and feedback loops can generate plausible daily travel activities and learn to adjust commute timing in a small proof-of-concept, pointing toward a new direction for agent-base...

  7. Large Language Model Powered Intelligent Urban Agents: Concepts, Capabilities, and Applications

    cs.MA 2025-07 conditional novelty 4.0 of 10

    The paper defines urban LLM agents, surveys their sensing, memory, reasoning, execution, and learning workflows, and organizes their applications across planning, transportation, environment, safety, and society.

Reference graph

Works this paper leans on

42 extracted references · 19 canonical work pages · cited by 7 Pith papers

  1. [1]

    Gpt-4 technical report

    Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774, 2023

    arXiv 2023

  2. [2]

    Everyday life sociology

    Patricia A Adler, Peter Adler, and Andrea Fontana. Everyday life sociology. Annual Review of Sociology, 13 0 (1): 0 217--235, 1987

    work page 1987

  3. [3]

    Simadl: Simulated activities of daily living dataset

    Talal Alshammari, Nasser Alshammari, Mohamed Sedky, and Chris Howard. Simadl: Simulated activities of daily living dataset. Data, 3 0 (2): 0 11, 2018

    work page 2018

  4. [4]

    A survey on evaluation of large language models

    Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, et al. A survey on evaluation of large language models. ACM Transactions on Intelligent Systems and Technology, 15 0 (3): 0 1--45, 2024

    2024

  5. [5]

    LangChain , October 2022

    Harrison Chase. LangChain , October 2022. URL https://github.com/langchain-ai/langchain

    work page 2022

  6. [6]

    Augmenting autotelic agents with large language models

    C \'e dric Colas, Laetitia Teodorescu, Pierre-Yves Oudeyer, Xingdi Yuan, and Marc-Alexandre C \^o t \'e . Augmenting autotelic agents with large language models. In Conference on Lifelong Learning Agents, pp.\ 205--226. PMLR, 2023

    work page 2023

  7. [7]

    Learning to simulate human mobility

    Jie Feng, Zeyu Yang, Fengli Xu, Haisu Yu, Mudan Wang, and Yong Li. Learning to simulate human mobility. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp.\ 3426--3433, 2020

    work page 2020

  8. [8]

    What is artificial intelligence? Springer, 1990

    James H Fetzer and James H Fetzer. What is artificial intelligence? Springer, 1990

    work page 1990

Show all 42 references
  1. [9]

    a rling and J \

    Tommy G \"a rling and J \"o rgen Garvill. Psychological explanations of participation in everyday activities. In Advances in Psychology, volume 96, pp.\ 270--297. Elsevier, 1993

    1993

  2. [10]

    Evaluation of virtual agents utilizing theory of mind in a real time action game

    Mark Hoogendoorn and Jeremy Soumokil. Evaluation of virtual agents utilizing theory of mind in a real time action game. In Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1-Volume 1, pp.\ 59--66, 2010

    2010

  3. [11]

    Bal \'a zs Korm \'a nyos and B \'e la Pataki. Multilevel simulation of daily activities: Why and how? In 2013 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), pp.\ 1--6. IEEE, 2013

    2013

  4. [12]

    Camel: Communicative agents for" mind" exploration of large language model society

    Guohao Li, Hasan Hammoud, Hani Itani, Dmitrii Khizbullin, and Bernard Ghanem. Camel: Communicative agents for" mind" exploration of large language model society. Advances in Neural Information Processing Systems, 36: 0 51991--52008, 2023

    2023

  5. [13]

    Training socially aligned language models on simulated social interactions

    Ruibo Liu, Ruixin Yang, Chenyan Jia, Ge Zhang, Denny Zhou, Andrew M Dai, Diyi Yang, and Soroush Vosoughi. Training socially aligned language models on simulated social interactions. arXiv preprint arXiv:2305.16960, 2023

    2023 arXiv

  6. [14]

    Practical synthetic human trajectories generation based on variational point processes

    Qingyue Long, Huandong Wang, Tong Li, Lisi Huang, Kun Wang, Qiong Wu, Guangyu Li, Yanping Liang, Li Yu, and Yong Li. Practical synthetic human trajectories generation based on variational point processes. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery ...

    2023

  7. [15]

    Forecasting and uncertainty in the economic and business world

    Spyros Makridakis, Robin M Hogarth, and Anil Gaba. Forecasting and uncertainty in the economic and business world. International Journal of Forecasting, 25 0 (4): 0 794--812, 2009

    2009

  8. [16]

    Yohei Nakajima. babyagi. https://github.com/yoheinakajima/babyagi, 2023. Accessed: 2024-09-29

    2023

  9. [17]

    Generative agents: Interactive simulacra of human behavior

    Joon Sung Park, Joseph O'Brien, Carrie Jun Cai, Meredith Ringel Morris, Percy Liang, and Michael S Bernstein. Generative agents: Interactive simulacra of human behavior. In Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology, pp.\ 1--22, 2023

    2023

  10. [18]

    Turing test: 50 years later

    Ayse Pinar Saygin, Ilyas Cicekli, and Varol Akman. Turing test: 50 years later. Minds and Machines, 10 0 (4): 0 463--518, 2000

    2000

  11. [19]

    Hugginggpt: Solving ai tasks with chatgpt and its friends in hugging face

    Yongliang Shen, Kaitao Song, Xu Tan, Dongsheng Li, Weiming Lu, and Yueting Zhuang. Hugginggpt: Solving ai tasks with chatgpt and its friends in hugging face. Advances in Neural Information Processing Systems, 36, 2024

    2024

  12. [20]

    AutoGPT , 2023

    Significant Gravitas . AutoGPT , 2023. URL https://github.com/Significant-Gravitas/AutoGPT

    2023

  13. [21]

    A systematic study on reinforcement learning based applications

    Keerthana Sivamayil, Elakkiya Rajasekar, Belqasem Aljafari, Srete Nikolovski, Subramaniyaswamy Vairavasundaram, and Indragandhi Vairavasundaram. A systematic study on reinforcement learning based applications. Energies, 16 0 (3): 0 1512, 2023

    2023

  14. [22]

    Llm-planner: Few-shot grounded planning for embodied agents with large language models

    Chan Hee Song, Jiaman Wu, Clayton Washington, Brian M Sadler, Wei-Lun Chao, and Yu Su. Llm-planner: Few-shot grounded planning for embodied agents with large language models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp.\ 2998--3009, 2023

    2023

  15. [23]

    Qwen2.5: A party of foundation models, September 2024

    Qwen Team. Qwen2.5: A party of foundation models, September 2024. URL https://qwenlm.github.io/blog/qwen2.5/

    2024

  16. [24]

    Generative agent-based modeling with actions grounded in physical, social, or digital space using concordia

    Alexander Sasha Vezhnevets, John P Agapiou, Avia Aharon, Ron Ziv, Jayd Matyas, Edgar A Du \'e \ n ez-Guzm \'a n, William A Cunningham, Simon Osindero, Danny Karmon, and Joel Z Leibo. Generative agent-based modeling with actions grounded in physical, social, or digital space us...

    2023 arXiv

  17. [25]

    A review of agent and service-oriented concepts applied to intelligent energy systems

    Pavel Vrba, Vladim \' r Ma r \' k, Pierluigi Siano, Paulo Leit \ a o, Gulnara Zhabelova, Valeriy Vyatkin, and Thomas Strasser. A review of agent and service-oriented concepts applied to intelligent energy systems. IEEE Transactions on Industrial Informatics, 10 0 (3): 0 1890--...

    1903

  18. [26]

    Modeling autobiographical memory in human-like autonomous agents

    Di Wang, Ah-Hwee Tan, and Chunyan Miao. Modeling autobiographical memory in human-like autonomous agents. In Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems, pp.\ 845–853, 2016

    2016

  19. [27]

    Voyager: An open-ended embodied agent with large language models

    Guanzhi Wang, Yuqi Xie, Yunfan Jiang, Ajay Mandlekar, Chaowei Xiao, Yuke Zhu, Linxi Fan, and Anima Anandkumar. Voyager: An open-ended embodied agent with large language models. arXiv preprint arXiv:2305.16291, 2023 a

    2023 arXiv

  20. [28]

    Large language models as urban residents: An llm agent framework for personal mobility generation

    Jiawei Wang, Renhe Jiang, Chuang Yang, Zengqing Wu, Makoto Onizuka, Ryosuke Shibasaki, Noboru Koshizuka, and Chuan Xiao. Large language models as urban residents: An llm agent framework for personal mobility generation. Advances in Neural Information Processing Systems, 2024 a

    2024

  21. [29]

    A survey on large language model based autonomous agents

    Lei Wang, Chen Ma, Xueyang Feng, Zeyu Zhang, Hao Yang, Jingsen Zhang, Zhiyuan Chen, Jiakai Tang, Xu Chen, Yankai Lin, et al. A survey on large language model based autonomous agents. Frontiers of Computer Science, 18 0 (6): 0 186345, 2024 b

    2024

  22. [30]

    Humanoid agents: Platform for simulating human-like generative agents

    Zhilin Wang, Yu Ying Chiu, and Yu Cheung Chiu. Humanoid agents: Platform for simulating human-like generative agents. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pp.\ 167--176, 2023 b

    2023

  23. [31]

    The rise and potential of large language model based agents: A survey

    Zhiheng Xi, Wenxiang Chen, Xin Guo, Wei He, Yiwen Ding, Boyang Hong, Ming Zhang, Junzhe Wang, Senjie Jin, Enyu Zhou, et al. The rise and potential of large language model based agents: A survey. arXiv preprint arXiv:2309.07864, 2023

    2023 arXiv

  24. [32]

    Qwen2 technical report

    An Yang, Baosong Yang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Zhou, Chengpeng Li, Chengyuan Li, Dayiheng Liu, Fei Huang, et al. Qwen2 technical report. arXiv preprint arXiv:2407.10671, 2024

    2024 arXiv

  25. [33]

    React: Synergizing reasoning and acting in language models

    Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik R Narasimhan, and Yuan Cao. React: Synergizing reasoning and acting in language models. In The Eleventh International Conference on Learning Representations, 2023

    2023

  26. [34]

    Tree of thoughts: Deliberate problem solving with large language models

    Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, and Karthik Narasimhan. Tree of thoughts: Deliberate problem solving with large language models. Advances in Neural Information Processing Systems, 36, 2024

    2024

  27. [35]

    Learning to simulate daily activities via modeling dynamic human needs

    Yuan Yuan, Huandong Wang, Jingtao Ding, Depeng Jin, and Yong Li. Learning to simulate daily activities via modeling dynamic human needs. In Proceedings of the ACM Web Conference 2023, pp.\ 906--916, 2023

    2023

  28. [36]

    A survey on the memory mechanism of large language model based agents

    Zeyu Zhang, Xiaohe Bo, Chen Ma, Rui Li, Xu Chen, Quanyu Dai, Jieming Zhu, Zhenhua Dong, and Ji-Rong Wen. A survey on the memory mechanism of large language model based agents. arXiv preprint arXiv:2404.13501, 2024

    2024 arXiv

  29. [37]

    Cognitive psychology-based artificial intelligence review

    Jian Zhao, Mengqing Wu, Liyun Zhou, Xuezhu Wang, and Jian Jia. Cognitive psychology-based artificial intelligence review. Frontiers in Neuroscience, 16: 0 1024316, 2022

    2022

  30. [38]

    Difftraj: Generating gps trajectory with diffusion probabilistic model

    Yuanshao Zhu, Yongchao Ye, Shiyao Zhang, Xiangyu Zhao, and James Yu. Difftraj: Generating gps trajectory with diffusion probabilistic model. Advances in Neural Information Processing Systems, 36: 0 65168--65188, 2023

    2023

  31. [39]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

  32. [40]

    @esa (Ref

    \@ifxundefined[1] #1\@undefined \@firstoftwo \@secondoftwo \@ifnum[1] #1 \@firstoftwo \@secondoftwo \@ifx[1] #1 \@firstoftwo \@secondoftwo [2] @ #1 \@temptokena #2 #1 @ \@temptokena \@ifclassloaded agu2001 natbib The agu2001 class already includes natbib coding, so you should ...

  33. [41]

    \@lbibitem[] @bibitem@first@sw\@secondoftwo \@lbibitem[#1]#2 \@extra@b@citeb \@ifundefined br@#2\@extra@b@citeb \@namedef br@#2 \@nameuse br@#2\@extra@b@citeb \@ifundefined b@#2\@extra@b@citeb @num @parse #2 @tmp #1 NAT@b@open@#2 NAT@b@shut@#2 \@ifnum @merge>\@ne @bibitem@firs...

  34. [42]

    @open @close @open @close and [1] URL: #1 \@ifundefined chapter * \@mkboth \@ifxundefined @sectionbib * \@mkboth * \@mkboth\@gobbletwo \@ifclassloaded amsart * \@ifclassloaded amsbook * \@ifxundefined @heading @heading NAT@ctr thebibliography [1] @ \@biblabel @NAT@ctr \@bibset...

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.