IJCAI 2023poster1 citations

Discounting in Strategy Logic

Munyque Mittelmann, Aniello Murano, Laurent Perrussel

Abstract

Discounting is an important dimension in multi-agent systems as long as we want to reason about strategies and time. It is a key aspect in economics as it captures the intuition that the far-away future is not as important as the near future. Traditional verification techniques allow to check whether there is a winning strategy for a group of agents but they do not take into account the fact that satisfying a goal sooner is different from satisfying it after a long wait. In this paper, we augment Strategy Logic with future discounting over a set of discounted functions D, denoted SL[D]. We consider “until” operators with discounting functions: the satisfaction value of a specification in SL[D] is a value in [0, 1], where the longer it takes to fulfill requirements, the smaller the satisfaction value is. We motivate our approach with classical examples from Game Theory and study the complexity of model-checking SL[D]-formulas.

Agent-based and Multi-agent Systems: MAS: Formal verification, validation and synthesisKnowledge Representation and Reasoning: KRR: Computational complexity of reasoningKnowledge Representation and Reasoning: KRR: Qualitative, geometric, spatial, and temporal reasoning
BibTeX
@inproceedings{ijcai2023p26,
  title     = {Discounting in Strategy Logic},
  author    = {Mittelmann, Munyque and Murano, Aniello and Perrussel, Laurent},
  booktitle = {Proceedings of the Thirty-Second International Joint Conference on
               Artificial Intelligence, {IJCAI-23}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Edith Elkind},
  pages     = {225--233},
  year      = {2023},
  month     = {8},
  note      = {Main Track},
  doi       = {10.24963/ijcai.2023/26},
  url       = {https://doi.org/10.24963/ijcai.2023/26},
}
Discounting in Strategy Logic · IJCAI 2023