{"type":"ScholarlyArticle","title":"Multi-Agent Systems with Quantitative Satisficing Goals","authors":[{"name":"Senthil Rajasekaran","slug":"senthil-rajasekaran","given":"Senthil","family":"Rajasekaran","affiliation":"Rice University"},{"name":"Suguman Bansal","slug":"suguman-bansal","given":"Suguman","family":"Bansal","affiliation":"Georgia Institute of Technology"},{"name":"Moshe Y. Vardi","slug":"moshe-y-vardi","given":"Moshe Y.","family":"Vardi","affiliation":"Rice University"}],"abstract":"In the study of reactive systems, qualitative properties are usually easier to model and analyze than quantitative properties. This is especially true in systems where mutually beneficial cooperation between agents is possible, such as multi-agent systems. The large number of possible payoffs available to agents in reactive systems with quantitative properties means that there are many scenarios in which agents deviate from mutually beneficial outcomes in order to gain negligible payoff improvements. This behavior often leads to less desirable outcomes for all agents involved. For this reason we study satisficing goals, derived from a decision-making approach aimed at meeting a good-enough outcome instead of pure optimization. By considering satisficing goals, we are able to employ efficient automata-based algorithms to find pure-strategy Nash equilibria. We then show that these algorithms extend to scenarios in which agents have multiple thresholds, providing an approximation of optimization while still retaining the possibility of mutually beneficial cooperation and efficient automata-based algorithms. Finally, we demonstrate a one-way correspondence between the existence of epsilon-equilibria and the existence of equilibria in games where agents have multiple thresholds.","doi":"10.24963/ijcai.2023/32","url":"https://ijcai.org/proceedings/2023/32","pdf":"https://ijcai.org/proceedings/2023/0032.pdf","markdown":"https://ijcai.org/proceedings/2023/32.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/32","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Agent-based and Multi-agent Systems","track":"Main Track","number":32,"pages":{"first":280,"last":288},"keywords":["Agent-based and Multi-agent Systems: MAS: Multi-agent planning","Agent-based and Multi-agent Systems: MAS: Formal verification, validation and synthesis"],"videos":[],"references":[{"position":1,"raw":"[Bansal and Vardi, 2019] S. 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