Mostexisting decision-theoretic planners represent uncertainty about the state of the world with a precisely specified probability distribution over world states. This representat...
In this paper we implement planning using answer set programming. We consider the action language A and its extensions. We show that when the domain is described using richer feat...
In multiagent planning, an agent sometimes needs to collaborate with others to construct complex plans, or to accomplish large organizational tasks which it cannot do alone. Since...
To learn to behave in highly complex domains, agents must represent and learn compact models of the world dynamics. In this paper, we present an algorithm for learning probabilist...
Hanna Pasula, Luke S. Zettlemoyer, Leslie Pack Kae...
A great deal of research has addressed the problem of generating optimal plans, but these plans are of limited use in circumstances where noisy sensors, unanticipated exogenous ac...