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 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...
Markov decision processes (MDPs) with discrete and continuous state and action components can be solved efficiently by hybrid approximate linear programming (HALP). The main idea ...
We define TTD-MDPs, a novel class of Markov decision processes where the traditional goal of an agent is changed from finding an optimal trajectory through a state space to realiz...
David L. Roberts, Mark J. Nelson, Charles Lee Isbe...
We present a major improvement to the incremental pruning algorithm for solving partially observable Markov decision processes. Our technique targets the cross-sum step of the dyn...