Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
A Game-Theoretic Perspective on Inconsistency Handling
Authors: Yakoub Salhi
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper introduces a game-theoretic framework for restoring consistency in propositional bases. The process is modeled as an interactive dialogue between two agents: a Proponent, who seeks to isolate a unique, consistent subset by posing strategic questions, and an Opponent, who aims to obstruct that goal through adversarial responses. We show that this framework provides a foundation for quantifying the effort involved in restoring consistency, revealing a connection between this effort and entropy in information theory. Finally, we demonstrate how the quantified restoration effort can serve as a basis for measuring inconsistency. |
| Researcher Affiliation | Academia | Yakoub Salhi Univ. Artois, CNRS, CRIL, F-62300 Lens, France EMAIL |
| Pseudocode | Yes | Algorithm 1 Greedy Algorithm for Approximating Iw mc Require: A PB K and the set of its MCSes S = MCS(K) 1: function BUILDTREE(S) 2: if |S| = 1 then 3: return a leaf node identifying the single MCS in S 4: end if 5: Let Ο = arg minΟ S S\T S G(Ο, S) 6: Recursively build left and right subtrees: Tl BUILDTREE(S Ο) Tr BUILDTREE(SΟ) 7: return the tree (Ο, Tl, Tr) 8: end function 9: BUILDTREE(S) |
| Open Source Code | No | The paper does not provide any statement about releasing source code, nor does it include links to a code repository. |
| Open Datasets | No | The paper introduces a game-theoretic framework and an inconsistency measure, but it does not conduct empirical experiments using specific datasets. Therefore, there is no mention of open datasets. |
| Dataset Splits | No | The paper focuses on theoretical development and does not describe any experiments that would involve dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup or specific hardware used for computations. |
| Software Dependencies | No | The paper describes a theoretical framework and an algorithm but does not specify any software dependencies or their versions. |
| Experiment Setup | No | The paper is theoretical and does not include an experimental section with specific setup details like hyperparameters or training configurations. |