We introduce and study SL[F]-a quantitative extension of SL (Strategy Logic), one of the most natural and expressive logics describing strategic behaviours. The satisfaction value of an SL[F] formula is a real value in [0, 1], reflecting “how much” or “how well” the strategic on-going objectives of the underlying agents are satisfied. We demonstrate the applications of SL[F] in quantitative reasoning about multi-agent systems, by showing how it can express concepts of stability in multi-agent systems, and how it generalises some fuzzy temporal logics. We also provide a model-checking algorithm for our logic, based on a quantitative extension of Quantified CTL?.

Reasoning about quality and fuzziness of strategic behaviours / Bouyer, P.; Kupferman, O.; Markey, N.; Maubert, B.; Murano, A.; Perelli, G.. - In: IJCAI. - ISSN 1045-0823. - 2019-:(2019), pp. 1588-1594. (Intervento presentato al convegno 28th International Joint Conference on Artificial Intelligence, IJCAI 2019 nel 2019) [10.24963/ijcai.2019/220].

Reasoning about quality and fuzziness of strategic behaviours

Maubert B.;Murano A.;
2019

Abstract

We introduce and study SL[F]-a quantitative extension of SL (Strategy Logic), one of the most natural and expressive logics describing strategic behaviours. The satisfaction value of an SL[F] formula is a real value in [0, 1], reflecting “how much” or “how well” the strategic on-going objectives of the underlying agents are satisfied. We demonstrate the applications of SL[F] in quantitative reasoning about multi-agent systems, by showing how it can express concepts of stability in multi-agent systems, and how it generalises some fuzzy temporal logics. We also provide a model-checking algorithm for our logic, based on a quantitative extension of Quantified CTL?.
2019
978-0-9992411-4-1
Reasoning about quality and fuzziness of strategic behaviours / Bouyer, P.; Kupferman, O.; Markey, N.; Maubert, B.; Murano, A.; Perelli, G.. - In: IJCAI. - ISSN 1045-0823. - 2019-:(2019), pp. 1588-1594. (Intervento presentato al convegno 28th International Joint Conference on Artificial Intelligence, IJCAI 2019 nel 2019) [10.24963/ijcai.2019/220].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/880513
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