Towards a pareto-optimal solution in general-sum games

Multiagent learning literature has investigated iterated twoplayer games to develop mechanisms that allow agents to learn to converge on Nash Equilibrium strategy profiles. Such equilibrium configuration implies that there is no motivation for one player to change its strategy if the other does not. Often, in general sum games, a higher payoff can be obtained by both players if one chooses not to respond optimally to the other player. By developing mutual trust, agents can avoid iterated best responses that will lead to a lesser payoff Nash Equilibrium. In this paper we work with agents who select actions based on expected utility calculations that incorporates the observed frequencies of the actions of the opponent(s). We augment this stochasticallygreedy agents with an interesting action revelation strategy that involves strategic revealing of one’s action to avoid worst-case, pessimistic moves. We argue that in certain situations, such apparently risky revealing can indeed pr...

• Type: Conference
• Year: 2003
• Where: ATAL
• Authors: Sandip Sen, Stéphane Airiau, Rajatish Mukherjee
• Keywords: ATAL 2003, Nash Equilibrium, Nash Equilibrium Strategy, Payoff Nash Equilibrium