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2008
Springer

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

8 years 4 months ago
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...
Doran Chakraborty, Sandip Sen
Added 12 Oct 2010
Updated 12 Oct 2010
Type Conference
Year 2008
Where ATAL
Authors Doran Chakraborty, Sandip Sen
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