Abstract. This paper presents a formulation of an optimality principle for a new class of concurrent decision systems formed by products of deterministic Markov decision processes ...
Abstract. We present a model for the multiagent patrolling problem with continuous-time. An anytime and online algorithm is then described and extended to asynchronous multiagent d...
This paper summarizes research on a new emerging framework for learning to plan using the Markov decision process model (MDP). In this paradigm, two approaches to learning to plan...
Sridhar Mahadevan, Sarah Osentoski, Jeffrey Johns,...
We present a learning framework for Markovian decision processes that is based on optimization in the policy space. Instead of using relatively slow gradient-based optimization al...
We propose a robust approach for aligning lecture slides with lecture videos using a combination of Hough transform, optical flow and Gabor analysis. A Markov Decision Process mod...