Reasoning about Quality and Fuzziness of Strategic Behaviours
Authors: Patricia Bouyer, Orna Kupferman, Nicolas Markey, Bastien Maubert, Aniello Murano, Giuseppe Perelli
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We introduce and study SL[F] a quantitative extension of SL (Strategy Logic)... We also provide a model-checking algorithm for our logic, based on a quantitative extension of Quantified CTL . A model-checking procedure for SL[F], which is our main contribution, enables formal reasoning about both quality and fuzziness of strategic behaviours... The model-checking problem for SL[F] formulas of nesting depth at most k is (k + 1)-EXPTIME-complete. This result, together with a reduction from SL[F] to BQCTL [F] that we present in Section ??, entails the decidability of model checking SL[F] announced in Theorem 1. |
| Researcher Affiliation | Academia | 1 LSV, CNRS & ENS Paris-Saclay, Univ. Paris-Saclay, France 2 Hebrew University, Israel 3 Irisa, CNRS & Inria & Univ. Rennes, France 4 Universit a degli Studi di Napoli Federico II , Italy 5 University of Leicester, UK |
| Pseudocode | No | The paper describes the algorithms and procedures conceptually and mathematically, but it does not include any formal pseudocode blocks or algorithm listings with specific labels like 'Algorithm 1' or 'Pseudocode'. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code for the described methodology, nor does it provide a direct link to a code repository. |
| Open Datasets | No | This is a theoretical paper that introduces and analyzes a logical formalism and its model-checking problem. It does not conduct empirical studies that would require training on a dataset. |
| Dataset Splits | No | This is a theoretical paper that introduces and analyzes a logical formalism and its model-checking problem. It does not conduct empirical studies that would require dataset splits for training, validation, or testing. |
| Hardware Specification | No | This is a theoretical paper focusing on logic and algorithm design/complexity. It does not describe any empirical experiments, and therefore no hardware specifications are provided. |
| Software Dependencies | No | The paper discusses various logical formalisms and theoretical concepts but does not mention specific software implementations or dependencies with version numbers that would be required for replication. |
| Experiment Setup | No | This is a theoretical paper that defines new logics and model-checking algorithms. It does not include empirical experiments, and thus no experimental setup details such as hyperparameters or system-level training settings are provided. |