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..
Verifying Existence of Resource-Bounded Coalition Uniform Strategies
Authors: Natasha Alechina, Mehdi Dastani, Brian Logan
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The main contribution of this paper is a decidable model-checking procedure for RB ATSEL with coalition-uniform strategies (wrt any decidable notion of indistinguishability). To prove decidability we give an algorithm which, given a structure M = (Φ, Agt, Res, S, , Act, d, c, δ) and a formula φ0, returns the set of states [φ0]M satisfying φ0: [φ0]M = {s | M, s |= φ0}. The theorem follows from Lemmas 1 and 2 which establish termination and correctness of the algorithm respectively. |
| Researcher Affiliation | Academia | Natasha Alechina University of Nottingham EMAIL Mehdi Dastani University of Utrecht EMAIL Brian Logan University of Nottingham EMAIL |
| Pseudocode | Yes | Algorithm 1 Labelling φ0; Algorithm 2 Labelling hh Abiiφ U; Algorithm 3 Labelling hh Abii2φ |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper is theoretical and focuses on logic and model-checking decidability. It does not involve empirical experiments with datasets that would require training, validation, or testing. |
| Dataset Splits | No | The paper is theoretical and focuses on logic and model-checking decidability. It does not involve empirical experiments with datasets that would require training, validation, or testing splits. |
| Hardware Specification | No | The paper does not mention any specific hardware used for experiments, as it is a theoretical work focusing on logical formalisms and algorithms. |
| Software Dependencies | No | The paper describes a logical formalism and algorithms but does not specify any software dependencies with version numbers for their implementation or application. |
| Experiment Setup | No | The paper is theoretical and presents algorithms and proofs. It does not describe an experimental setup with hyperparameters or training settings as it does not involve empirical model training. |