A Bayesian Knowledge Base is a generalization of traditional Bayesian Networks where nodes or groups of nodes have independence. In this paper we describe a method of generating a ...
We consider the fundamental problem of monitoring (i.e. tracking) the belief state in a dynamic system, when the model is only approximately correct and when the initial belief st...
Inverse Reinforcement Learning (IRL) is the problem of learning the reward function underlying a Markov Decision Process given the dynamics of the system and the behaviour of an e...
A long-lived agent continually faces new tasks in its environment. Such an agent may be able to use knowledge learned in solving earlier tasks to produce candidate policies for it...
We develop an exact dynamic programming algorithm for partially observable stochastic games (POSGs). The algorithm is a synthesis of dynamic programming for partially observable M...
Eric A. Hansen, Daniel S. Bernstein, Shlomo Zilber...