MB-AIM-FSI: a model based framework for exploiting gradient ascent multiagent learners in strategic interactions

Future agent applications will increasingly represent human users autonomously or semi-autonomously in strategic interactions with similar entities. Hence, there is a growing need to develop algorithmic approaches that can learn to recognize commonalities in opponent strategies and exploit such commonalities to improve strategic response. Recently a framework [9] has been proposed that aims for targeted optimality against a set of finite memory opponents. We propose an approach that aims for targeted optimality against the set of all possible multiagent learning algorithms that perform gradient search to select a single stage Nash Equilibria of a repeated game. Such opponents induce a Markov Decision Process as the learning environment and appropriate responses to such environments are learned by assuming a generative model of the environment. In the absence of a generative model, we present a framework, MBAIM-FSI, that models the opponent online based on interactions, solves the mode...

• Type: Conference
• Year: 2008
• Where: ATAL
• Authors: Doran Chakraborty, Sandip Sen
• Keywords: ATAL 2008, Intelligent Agents, Multiagent Learning, Strategic Interactions, Targeted Optimality