Markov Decision Processes are a powerful framework for planning under uncertainty, but current algorithms have difficulties scaling to large problems. We present a novel probabil...
In this paper, we describe the partially observable Markov decision process pomdp approach to nding optimal or near-optimal control strategies for partially observable stochastic ...
Anthony R. Cassandra, Leslie Pack Kaelbling, Micha...
Computers have already eclipsed the level of human play in competitive Scrabble, but there remains room for improvement. In particular, there is much to be gained by incorporating...
—We aim to characterize the maximum link throughput of a multi-channel opportunistic communication system. The states of these channels evolve as independent and identically dist...
Researchers often express probabilistic planning problems as Markov decision process models and then maximize the expected total reward. However, it is often rational to maximize ...