Video

Google TechTalks March 6, 2006 Eyal Amir ABSTRACT: Many complex domains offer limited information about their exact state and the way actions affect them. There, agents need to learn action models to act effectively, at the same time that they track the state of the domain. In this presentation I will describe polynomial-time algorithms for learning logical models of actions' effects and preconditions in deterministic partially observable domains. These algorithms represent the set of possible action models compactly, and update it after every action execution and partial observation. This approach is the first tractable learning algorithm for partially observable dynamic domains. I will mention recent extensions of this work to relational domains, and will also discuss potential applications of this work to agents playing adventure games and to active web mining. This is joint work with my students, Dafna Shahaf and Allen Chang.