Verifying Existence of Resource-Bounded Coalition Uniform Strategies

Authors: Natasha Alechina, Mehdi Dastani, Brian Logan

IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | 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 nza@cs.nott.ac.uk Mehdi Dastani University of Utrecht m.m.dastani@uu.nl Brian Logan University of Nottingham bsl@cs.nott.ac.uk
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.