Projecten per jaar
Samenvatting
We present a detailed analysis of Nash equilibria in multiobjective normalform games, which are normalform games with vectorial payoffs. Our approach is based on modelling each player's utility using a utility function that maps a vector to a scalar utility. For mixed strategies, we can apply the utility function before calculating the expectation of the payoff vector as well as after, resulting in two distinct optimisation criteria. We show that when computing the utility from the expected payoff, a Nash equilibrium can be guaranteed when players have quasiconcave utility functions. In addition, we show that when players have quasiconvex utility functions, pure strategy Nash equilibria are equal under both optimisation criteria. We extend this to settings where some players optimise for one criterion, while others optimise for the second. We combine these results and formulate an algorithm that computes all pure strategy Nash equilibria given quasiconvex utility functions.
Originele taal2  English 

Titel  The 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2023) 
Uitgeverij  International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS) 
Status  Accepted/In press  5 mrt 2023 
Projecten
 2 Actief

FWOTM1108: Besluitvorming in multiobjective multiagent domeinen met teambeloning
1/10/22 → 30/09/25
Project: Fundamenteel

FWOTM1082: Reinforcement Learning in MultiDoel MultiAgent Systemen
1/11/21 → 31/10/23
Project: Fundamenteel