On Logics of Strategic Ability Based on Propositional Control
Authors: Francesco Belardinelli, Andreas Herzig
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper we provide a contribution to the comparison of two popular frameworks: Concurrent Game Structures (CGS) and Coalition Logic of Propositional Control (CLPC). Specifically, we ground the abstract abilities of agents in CGS on Propositional Control, thus obtaining a class of CGS that has the same expressive power as CL-PC. We study the computational properties of this setting. |
| Researcher Affiliation | Academia | Francesco Belardinelli1 and Andreas Herzig2 1 IBISC, Univ. Evry, France 2 IRIT, Univ. Toulouse, France |
| Pseudocode | No | The paper is theoretical, presenting formal definitions, lemmas, and theorems. It does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper is theoretical and focuses on formal logic and complexity analysis. There is no mention of open-source code being released for the methodologies or systems described. |
| Open Datasets | No | This is a theoretical paper on logics and computational complexity. It does not involve empirical datasets or model training. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation sets or any form of dataset splitting (e.g., training, validation, test splits). |
| Hardware Specification | No | The paper is purely theoretical and focuses on formal logic and computational complexity. It does not describe any empirical experiments, and thus no hardware specifications for running experiments are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe any implemented systems or software that would require specific version numbers for reproducibility. |
| Experiment Setup | No | This is a theoretical paper that does not involve empirical experiments. Therefore, there are no experimental setup details, hyperparameters, or training configurations described. |