The overall aim of our research is to develop techniques to reason about the equilibrium properties of multi-agent systems. We model multi-agent systems as concurrent games, in which each player is a process that is assumed to act independently and strategically in pursuit of personal preferences. In this article, we study these games in the context of finite-memory strategies, and we assume players’ preferences are defined by a qualitative and a quantitative objective, which are related by a lexicographic order: a player first prefers to satisfy its qualitative objective (given as a formula of linear temporal logic) and then prefers to minimise costs (given by a mean-payoff function). Our main result is that deciding the existence of a strict $$epsilon $$ϵ Nash equilibrium in such games is 2ExpTime-complete (and hence decidable), even if players’ deviations are implemented as infinite-memory strategies.
2020, ACTA INFORMATICA, Pages -
Equilibria for Games with Combined Qualitative and Quantitative Objects (01a Articolo in rivista)
Gutierrez Juilan, Murano Aniello, Perelli Giuseppe, Rubin Sasha, Steeples Thomas, Wooldridge Michael
Gruppo di ricerca: Artificial Intelligence and Knowledge Representation