This paper presents an approach to building plans using partially observable Markov decision processes. The approach begins with a base solution that assumes full observability. T...
Abstract-This paper deals with enhancing the level of autonomy in a robotic work cell. With that mission in mind, we present here an integrated framework for the sensing, the plann...
Abstract. For large state-space Markovian Decision Problems MonteCarlo planning is one of the few viable approaches to find near-optimal solutions. In this paper we introduce a new...
For autonomous artificial decision-makers to solve realistic tasks, they need to deal with searching through large state and action spaces under time pressure. We study the probl...
The development of informative, admissible heuristics for cost-optimal planning remains a significant challenge in domain-independent planning research. Two techniques are commonl...