Trustworthy and Explainable Decision-Making for Workforce allocation

Andreas Strahl; Dimos Tsouros; Guillaume Pov\'eda; H\'el\`ene Verhaeghe; Ignace Bleukx; Mark Hall; Nahum Alvarez; Ryma Boumazouza; Santiago Quintana-Amate; Tias Guns

REVIEW 4 major objections 5 minor 16 references

Trustworthy and Explainable Decision-Making for Workforce allocation

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 The paper claims that infeasible workforce-allocation problems can be handled transparently by a constraint-programming tool that computes minimal conflicts (MUS) and minimal repairs (MCS), presents them visually, and lets planners…

desk verdict Honest tool paper with a clearly specified CP model; the interactive explainability story rests on a scaling assumption the authors themselves admit is untested. read the letter →

arxiv 2412.10272 v1 pith:N54BROWV submitted 2024-12-13 cs.AI

Guillaume Povéda , Ryma Boumazouza , Andreas Strahl , Mark Hall , Santiago Quintana-Amate , Nahum Alvarez , Ignace Bleukx , Dimos Tsouros

show 2 more authors

Hélène Verhaeghe Tias Guns

This is my paper · ORCID

classification cs.AI

keywords constraintprogrammingexplainableAIworkforceallocationschedulingminimalunsatisfiablesubset(MUS)correction(MCS)human-in-the-loopinfeasibilityrestoration

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

Workforce allocation in industry often fails because constraints—team availability, skills, non-overlap, travel time—cannot all be met at once, and standard solvers only return 'infeasible' with no reason. The paper is trying to establish that a constraint-programming tool can make such infeasibilities transparent: it computes an irreducible set of conflicting constraints (a MUS) or a minimal set to relax (an MCS), shows them on a Gantt chart, and lets planners restore feasibility interactively. Preliminary runs on generated instances up to 24 hours show that optimization finds optimal allocations in under a second with multiple search workers, and computing one explanation takes about a second. The stated aim is that planners will trust and accept automated schedules because they can see and control why an allocation fails.

What carries the argument

The machinery is a Boolean constraint model plus the MUS/MCS explanation pair. A MUS is an inclusion-minimal subset of constraints that is still unsatisfiable, showing the user the smallest conflict to attack. An MCS is an inclusion-minimal subset whose removal makes the whole constraint set satisfiable, showing the user the cheapest global repair. The tool computes these with deletion-based extraction on a pseudo-Boolean solver running under assumptions, and visualizes the result by solving a size-maximal satisfiable subproblem, drawing allocated tasks in a Gantt chart and putting unallocated tasks on an 'Unset' line. A third mechanism rewrites the 'every task must be allocated' constraint as a weighted objective, so users can tune task priorities and have the solver drop low-weight tasks first.

What would settle it

Generate infeasible instances at 100, 500, and 1000 activities with the same constraint types and measure the time to produce one MUS; if average explanation time grows steeply (for example, from seconds to minutes) while optimization remains fast, the interactive promise of the tool fails at industrial scale.

Watch

Extended reading notes

Core claim

The central claim is that infeasibility is not a dead end but material for an interactive dialogue. On a Boolean constraint model where every activity must be assigned exactly one compatible team and no team overlaps its tasks, the tool detects unsatisfiability and mines a Minimal Unsatisfiable Subset—a smallest set of constraints that cannot be satisfied together—then lets the user relax constraints step by step, apply a Minimal Correction Subset globally, or re-prioritize tasks so that a weighted Max-CSP solver drops lower-priority allocations first. On the paper's generated benchmark set, the explanation calculation stays within 0.6–1.13 seconds and explanations stay around 10 constraints regardless of instance length, which supports the claim that explainable conflict resolution is computationally practical at the tested scale.

Load-bearing premise

The weakest load-bearing premise is that the explanation computations stay fast enough on real industrial instances, which can reach roughly a thousand activities, even though the experiments here cover only a few dozen.

Editorial extensions

If this is right

If the tool works as described, planners no longer face a binary feasible/infeasible answer; they get a concrete set of constraints to examine and relax.
Interactive MCS relaxation can be iterative: after a user accepts only part of a correction set, the tool recomputes a new MCS, supporting mix-and-match repairs.
The weighted Max-CSP formulation gives a direct way to encode planner expertise as task priorities, turning feasibility restoration into an optimization over user preferences.
Preliminary timing suggests MUS computation scales acceptably for instances up to 24 hours and a few dozen activities, but the paper itself notes that further experiments are needed.
The prototype is intended as a basis for future user studies measuring understandability and acceptance, not just computation time.

Reading between the lines

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

The paper leaves implicit that the three restoration mechanisms are complementary: MUS resolution gives local control, MCS gives global minimal repair, and priority tuning gives preference-driven repair; a natural hypothesis is that different user roles will prefer different mechanisms.
If instance size grows to the stated industrial scale of up to roughly a thousand activities, MUS/MCS computation may need decomposition or step-wise explanations; the paper flags this as future work on the path to a scheduling model.
A testable extension would be to measure whether users who interactively relax constraints produce solutions closer to their implicit planning knowledge than users who accept a solver's optimal solution without explanation.
The 'Unset' visualization trick—solving a size-maximal satisfiable subproblem to draw a Gantt chart for an infeasible instance—could be reused in other scheduling explainability interfaces.

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.

Desk Editor's Note

read the letter

Bottom line: this is an honest tool/position paper, not a results paper. The thing to know: the prototype is real and the CP model is clearly specified; the thing that worries me is that the one number that would make the interactive explainability story work — explanation runtime at the stated industrial scale (up to ~1000 activities) — is missing. The stress-test note lands.

What's new is modest. MUS/MCS-based infeasibility explanation is standard, and interactive feasibility restoration has been done before (they cite Senthooran et al. 2021). The contribution is the integration into a concrete workforce-allocation tool with a Gantt-based conflict view and three interaction modes: MUS-by-MUS, MCS with re-computation, and weighted priorities. That integration is useful for the explainable-CP community as a worked example, and the paper is transparent that this is an ongoing project with evaluation still to come.

Where it's soft: (1) Scalability. Table 2 stops at 24-hour instances with 'a few dozen activities'; the stated industrial range goes to ~1000. Deletion-based MUS extraction solves once per constraint, and the MCS re-computation loop in Section 3.2 repeats that. The authors flag this themselves in Section 5, and Section 4.1.2 says 'further experiments are necessary.' That is not fatal in a paper that is explicitly preliminary, but it is the load-bearing gap. (2) The reported numbers are thin: Table 1 has an 'optimal' column that is never defined (fraction of instances solved to optimality? something else?), and there are no standard deviations or per-instance breakdowns. For 20 instances, means without spread are hard to read. (3) No artifact link and no user study, so 'trustworthiness' remains a design goal rather than a demonstrated property. The paper says as much, which is fine, but the title promises a bit more than the content delivers.

The citation pattern looks fine, and the model description is precise enough to reimplement. There is no circularity and nothing that looks invented.

Who this is for: people working on explainable CP and human-in-the-loop scheduling who want a concrete example of MUS/MCS interactivity in an industrial context. It deserves a serious referee at a venue that accepts application/system papers about ongoing work, with the expectation that the authors tighten the empirical reporting and, ideally, add a scaling experiment along the lines of the stress-test note.

Referee Report

4 major / 5 minor

Summary. The paper presents an ongoing project on a constraint programming (CP) based decision-support tool for workforce allocation, with a focus on explainability and trustworthiness. The authors define a Boolean CP model with allocation, non-overlap, compatibility, same-team, and used-team constraints, plus redundant clique and symmetry-breaking constraints. The tool computes and visualizes conflicts via minimal unsatisfiable subsets (MUS) and supports interactive feasibility restoration through local MUS resolution, global minimal correction subset (MCS) guidance, and weighted task-priority fine-tuning. A prototype implemented with CPMpy and Streamlit is described, and preliminary experiments report optimization times and MUS computation times on generated instances of 6, 8, and 24 hours. The paper concludes with planned user studies and a scheduling extension.

Significance. If the prototype performs as claimed, the work is a useful industrial application paper combining standard explainable CP techniques (MUS/MCS) with a human-in-the-loop interface for a realistic workforce allocation problem. The CP model is precisely specified, the MUS/MCS definitions are standard, and the paper clearly acknowledges open limitations, including the need for user studies and scalability challenges. The main value is in demonstrating how existing XAI-for-CP components can be assembled into an interactive tool and in identifying concrete gaps for future work. The contribution is not a new algorithm or proof, and the current empirical evidence is preliminary, so the significance depends on whether the interactive conflict-resolution claim can be shown to hold at the stated industrial scale and with users.

major comments (4)

[Section 4.1.2, Table 2] The explainability experiments are run only on instances described as 'a few dozen activities', yet Section 4 states that the industrial use case ranges up to about one thousand activities with |W|≈20. The deletion-based MUS algorithm described in Section 4.1.2 requires one solver call per constraint, and the MCS-based interactive workflow in Section 3.2 re-computes an MCS after each user relaxation; neither cost is measured at the stated upper range. Please report explicit instance sizes in terms of numbers of activities, teams, and constraints, and provide scaling experiments (e.g., 50, 200, 500, 1000 activities) with percentiles or worst-case times rather than only means. Without this, the 'interactive' restoration claim cannot be transferred to the industrial setting.
[Section 4.1.1, Table 1] The column labeled 'optimal' is never defined in the text or the table caption; it appears to be the fraction of instances proven optimal within the 30-second timeout, but this must be stated explicitly. Because the reported t_solve values mix complete solves with timeout-censored runs, the mean is not a clean measure of computation time; report the success rate separately, with medians and quantiles, and state the timeout for each row. The discussion of the symmetry constraints is also ambiguous: in the multi-worker rows, adding symmetry increases t_solve (e.g., 0.06 to 0.18 for len 6), while the text says symmetry and redundant constraints have 'a clear negative effect' on initialization and solving; clarify whether the negative effect claim refers to t_solve or t_total, and distinguish it from the mono-worker setting where symmetry improves the optimality rate.
[Section 4.2.4] The statement in the Local Conflict Resolution part that 'few iterations were required to restore feasibility, and we surmise that this observation remains true for real scenarios' is not supported by any reported experiment or iteration count. If this observation is intended to support the practicality of the interactive MUS workflow, provide the number of instances, the number of iterations, and the user actions involved; otherwise, remove the conjecture or clearly mark it as an anecdote.
[Sections 1, 3, and 5] The title and abstract claim 'trustworthy' decision-making, but the paper reports no user study or established trust metric; Section 5 explicitly defers user studies to future work. The manuscript should either narrow the claim to the system's potential for trustworthiness and explainability, or include a preliminary evaluation with expert users. This is not a request to prove the psychology of trust, but the current wording overstates what the evidence supports, especially since the abstract describes the tool as 'intended to be evaluated'.

minor comments (5)

[Table 1 and Definition 2] There are typos in Table 1 ('T rue', 'F alse', 'T rue T rue') and in Definition 2 ('an only if' should be 'if and only if').
[Section 4] The phrase 'a few dozen activities' is vague; please report the exact numbers of activities used in the generated instances, as well as the numbers of teams and constraints, so the results are reproducible.
[Section 2.2, constraint 6b] The symmetry-breaking constraint is described as 'lexleader' but never formally defined; either give the definition or cite the exact formulation used.
[General] No code or data repository is provided, which makes the prototype and experiments difficult to reproduce; consider adding a link to the demonstrator or an anonymized experiment package.
[Table 2] The average explanation length is exactly 10 in all three rows, with no reported variance or instance-level detail; report the distribution or at least the standard deviation, and state how many instances were included per row.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the paper reports a prototype and benchmarks standard MUS/MCS algorithms; no fitted input is relabeled as a prediction.

full rationale

This is a systems/XAI paper, not a derivation. The CP model in Section 2 is a standard formulation, and the explanation methods in Section 3 use textbook definitions of MUS and MCS (Definitions 1 and 2) together with the well-known deletion-based MUS extraction algorithm cited from Marques-Silva [25]. The only empirical claims are runtime and explanation-length measurements in Tables 1 and 2, which are direct measurements on generated instances, not outputs predicted from fitted parameters. Self-citations such as CPMpy [11], step-wise explanations [1], and constraint acquisition [38] supply implementation libraries or future-work directions; none is used as a uniqueness theorem, as an unverified premise that forces the paper's conclusions, or as a renamed prediction. The paper explicitly flags its own limitations in Sections 4.1.2 and 5 ('further experiments are necessary to draw definitive conclusions'; 'this raises interesting scalability challenges for the XAI technology bricks such as MUS computation'), which is a correctness-risk caveat rather than circular reasoning. There is no equation whose definition presupposes the claimed result, no fitted input is presented as a prediction, and no load-bearing self-citation chain exists. Accordingly, the circularity score is 0.

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

The paper makes no pretense of a from-first-principles derivation. Its contribution is a system built on standard CP and XAI methods. The load-bearing assumptions are the realism of the generated instances and the untested claim that interactive explanations build trust. No new entities such as particles or forces are introduced.

assumptions (4)

domain assumption The generated instances accurately reflect a real-world workforce allocation scenario derived from historical data.
Stated in Section 4.1: the instances 'were generated to reflect a real-world scenario with specific constraints and conditions derived from historical data.' No evidence is provided that they match the true distribution or scale of industrial data.
standard math MUS and MCS computation algorithms (deletion-based, Exact solver under assumptions) correctly return minimal unsatisfiable and minimal correction subsets.
The paper relies on known algorithms for computing MUS/MCS in Section 4.1.2 and cites the relevant literature.
domain assumption Allowing users to interactively relax constraints from MUS/MCS and adjust task priorities will increase trust and facilitate conflict resolution.
This is the central motivating premise of the tool, presented in Sections 1 and 3.2, but it is not empirically tested. The paper states in Section 5 that user studies are planned future work.
domain assumption The constraint model correctly captures the operational rules of the industrial use case, including non-overlap, compatibility, and same-allocation constraints.
The model in Section 2 is a mathematical abstraction of the real problem; the paper does not confirm with domain experts that all relevant implicit constraints (e.g., unstated planner rules) are captured. Section 5 acknowledges that implicit constraints may be missing.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Trustworthy and Explainable Decision-Making for Workforce allocation." pith.science (2026). https://pith.science/paper/N54BROWV

@misc{pith2026241210272,
  author       = {Pith},
  title        = {Pith review of: Trustworthy and Explainable Decision-Making for Workforce allocation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/N54BROWV}},
  note         = {Machine review of arXiv:2412.10272}
}

read the original abstract

In industrial contexts, effective workforce allocation is crucial for operational efficiency. This paper presents an ongoing project focused on developing a decision-making tool designed for workforce allocation, emphasising the explainability to enhance its trustworthiness. Our objective is to create a system that not only optimises the allocation of teams to scheduled tasks but also provides clear, understandable explanations for its decisions, particularly in cases where the problem is infeasible. By incorporating human-in-the-loop mechanisms, the tool aims to enhance user trust and facilitate interactive conflict resolution. We implemented our approach on a prototype tool/digital demonstrator intended to be evaluated on a real industrial scenario both in terms of performance and user acceptability.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

16 extracted references · 13 canonical work pages

[1]

Simplifying step-wise explanation sequences

1 Ignace Bleukx, Jo Devriendt, Emilio Gamba, Bart Bogaerts, and Tias Guns. Simplifying step-wise explanation sequences. In29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Schloss Dagstuhl-Leibniz-Zentrum für Informatik,

work page 2023
[4]

8 Jo Devriendt, Bart Bogaerts, Broes De Cat, Marc Denecker, and Christopher Mears

URL:https://gitlab.com/JoD/exact. 8 Jo Devriendt, Bart Bogaerts, Broes De Cat, Marc Denecker, and Christopher Mears. Symmetry propagation: Improved dynamic symmetry breaking in SAT. InIEEE 24th International Conference on Tools with Artificial Intelligence, ICTAI 2012, Athens, Greece, November 7-9, 2012, pages 49–56. IEEE Computer Society, 2012.doi:10.110...

work page doi:10.1109/ictai.2012.16 2012
[8]

gurobi.com

URL:https://www. gurobi.com. 14 Alexey Ignatiev, Antonio Morgado, and Joao Marques-Silva. PySAT: A Python toolkit for pro- totyping with SAT oracles. InSAT, pages 428–437, 2018.doi:10.1007/978-3-319-94144-8_

work page doi:10.1007/978-3-319-94144-8_ 2018
[10]

Debugging unsatisfiable constraint models

16 Trustworthy and Explainable Decision-Making for Workforce allocation 21 Kevin Leo and Guido Tack. Debugging unsatisfiable constraint models. InIntegration of AI and OR Techniques in Constraint Programming: 14th International Conference, CPAIOR 2017, Padua, Italy, June 5-8, 2017, Proceedings 14, pages 77–93. Springer,

work page 2017
[11]

Or-tools

30 Laurent Perron. Or-tools. 31 Laurent Perron, Frédéric Didier, and Steven Gay. The CP-SAT-LP Solver. In Roland H. C. Yap, editor,29th International Conference on Principles and Practice of Constraint Programming (CP 2023), volume 280 ofLeibniz International Proceedings in Informatics (LIPIcs), pages 3:1–3:2, Dagstuhl, Germany,

work page 2023
[12]

URL: https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs

Schloss Dagstuhl – Leibniz-Zentrum für Informatik. URL: https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs. CP.2023.3, doi:10.4230/LIPIcs.CP.2023.3. 32 Laurent Perron and Vincent Furnon. Or-tools, 11

doi:10.4230/lipics 2023
[13]

35 Wojciech Samek, Grégoire Montavon, Andrea Vedaldi, Lars Kai Hansen, and Klaus Robert Müller

URL: https://www.sciencedirect.com/science/bookseries/15746526/2. 35 Wojciech Samek, Grégoire Montavon, Andrea Vedaldi, Lars Kai Hansen, and Klaus Robert Müller. Explainable ai–preface. InExplainable AI: Interpreting, Explaining and Visualizing Deep Learning, pages v–vii. Springer,

arXiv
[14]

Human-centred feasibility restoration

36 Ilankaikone Senthooran, Gleb Belov, Kevin Leo, Michael Wybrow, Matthias Klapperstueck, Tobias Czauderna, Mark Wallace, and Maria Garcia De La Banda. Human-centred feasibility restoration. InInternational Conference on Principles and Practice of Constraint Programming 2021, page

work page 2021

Show all 16 references

[2002]

Human-understandable explanations of infeasibility for resource-constrained scheduling problems

20 Niklas Lauffer and Ufuk Topcu. Human-understandable explanations of infeasibility for resource-constrained scheduling problems. InICAPS 2019 Workshop XAIP,

2019
[2006]

Increasing modeling language convenience with a universal n-dimensional array, cppy as python-embedded example

11 Tias Guns. Increasing modeling language convenience with a universal n-dimensional array, cppy as python-embedded example. In Proceedings of the 18th workshop on Constraint Modelling and Reformulation at CP (Modref 2019), volume 19,

2019
[2007]

6 Kristijonas Cyras, Ramamurthy Badrinath, Swarup Kumar Mohalik, Anusha Mujumdar, Alexandros Nikou, Alessandro Previti, Vaishnavi Sundararajan, and Aneta Vulgarakis Feljan

URL: https://books.google.fr/books?id=BiBgz6AIRpMC. 6 Kristijonas Cyras, Ramamurthy Badrinath, Swarup Kumar Mohalik, Anusha Mujumdar, Alexandros Nikou, Alessandro Previti, Vaishnavi Sundararajan, and Aneta Vulgarakis Feljan. Machine reasoning explainability.arXiv preprint arXi...

2009 arXiv
[2018]

10 M Sinan Gönül, Dilek Önkal, and Michael Lawrence

doi:10.24963/ijcai.2018/180. 10 M Sinan Gönül, Dilek Önkal, and Michael Lawrence. The effects of structural characteristics of explanations on use of a dss.Decision support systems, 42(3):1481–1493,

2018 doi
[2019]

Finding counterfactual explanations through constraint relaxations.arXiv preprint arXiv:2204.03429,

12 Sharmi Dev Gupta, Begum Genc, and Barry O’Sullivan. Finding counterfactual explanations through constraint relaxations.arXiv preprint arXiv:2204.03429,

arXiv
[2021]

Explainability fact sheets: A framework for systematic assessment of explainable approaches

37 Kacper Sokol and Peter Flach. Explainability fact sheets: A framework for systematic assessment of explainable approaches. In Proceedings of the 2020 conference on fairness, accountability, and transparency, pages 56–67,

2020
[2023]

Povéda et al

G. Povéda et al. 15 2 Ignace Bleukx, Tias Guns, and Dimos Tsouros. Explainable Constraint Solving: A hands-on tutorial, February 2024.doi:10.5281/zenodo.10694140. 3 Ryma Boumazouza, Fahima Cheikh-Alili, Bertrand Mazure, and Karim Tabia. A symbolic approach for counterfactual e...

2024 doi
[2024]

1609/aaai.v38i8.28655

URL: https://ojs.aaai.org/index.php/AAAI/article/view/28655, doi:10. 1609/aaai.v38i8.28655. 39 Toby Walsh. General symmetry breaking constraints. In Frédéric Benhamou, editor,Principles and Practice of Constraint Programming - CP 2006, 12th International Conference, CP 2006, G...

doi

Pith tools

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

[1] [1]

Simplifying step-wise explanation sequences

1 Ignace Bleukx, Jo Devriendt, Emilio Gamba, Bart Bogaerts, and Tias Guns. Simplifying step-wise explanation sequences. In29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Schloss Dagstuhl-Leibniz-Zentrum für Informatik,

work page 2023

[2] [4]

8 Jo Devriendt, Bart Bogaerts, Broes De Cat, Marc Denecker, and Christopher Mears

URL:https://gitlab.com/JoD/exact. 8 Jo Devriendt, Bart Bogaerts, Broes De Cat, Marc Denecker, and Christopher Mears. Symmetry propagation: Improved dynamic symmetry breaking in SAT. InIEEE 24th International Conference on Tools with Artificial Intelligence, ICTAI 2012, Athens, Greece, November 7-9, 2012, pages 49–56. IEEE Computer Society, 2012.doi:10.110...

work page doi:10.1109/ictai.2012.16 2012

[3] [8]

gurobi.com

URL:https://www. gurobi.com. 14 Alexey Ignatiev, Antonio Morgado, and Joao Marques-Silva. PySAT: A Python toolkit for pro- totyping with SAT oracles. InSAT, pages 428–437, 2018.doi:10.1007/978-3-319-94144-8_

work page doi:10.1007/978-3-319-94144-8_ 2018

[4] [10]

Debugging unsatisfiable constraint models

16 Trustworthy and Explainable Decision-Making for Workforce allocation 21 Kevin Leo and Guido Tack. Debugging unsatisfiable constraint models. InIntegration of AI and OR Techniques in Constraint Programming: 14th International Conference, CPAIOR 2017, Padua, Italy, June 5-8, 2017, Proceedings 14, pages 77–93. Springer,

work page 2017

[5] [11]

Or-tools

30 Laurent Perron. Or-tools. 31 Laurent Perron, Frédéric Didier, and Steven Gay. The CP-SAT-LP Solver. In Roland H. C. Yap, editor,29th International Conference on Principles and Practice of Constraint Programming (CP 2023), volume 280 ofLeibniz International Proceedings in Informatics (LIPIcs), pages 3:1–3:2, Dagstuhl, Germany,

work page 2023

[6] [12]

URL: https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs

Schloss Dagstuhl – Leibniz-Zentrum für Informatik. URL: https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs. CP.2023.3, doi:10.4230/LIPIcs.CP.2023.3. 32 Laurent Perron and Vincent Furnon. Or-tools, 11

doi:10.4230/lipics 2023

[7] [13]

35 Wojciech Samek, Grégoire Montavon, Andrea Vedaldi, Lars Kai Hansen, and Klaus Robert Müller

URL: https://www.sciencedirect.com/science/bookseries/15746526/2. 35 Wojciech Samek, Grégoire Montavon, Andrea Vedaldi, Lars Kai Hansen, and Klaus Robert Müller. Explainable ai–preface. InExplainable AI: Interpreting, Explaining and Visualizing Deep Learning, pages v–vii. Springer,

arXiv

[8] [14]

Human-centred feasibility restoration

36 Ilankaikone Senthooran, Gleb Belov, Kevin Leo, Michael Wybrow, Matthias Klapperstueck, Tobias Czauderna, Mark Wallace, and Maria Garcia De La Banda. Human-centred feasibility restoration. InInternational Conference on Principles and Practice of Constraint Programming 2021, page

work page 2021