We propose a new decision-theoretic approach for solving execution-time deliberation scheduling problems using recent advances in Generalized Semi-Markov Decision Processes (GSMDP...
—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...
Partially Observable Markov Decision Processes (POMDPs) have succeeded in planning domains that require balancing actions that increase an agent's knowledge and actions that ...
Abstract--Feature selection is an important challenge in machine learning. Unfortunately, most methods for automating feature selection are designed for supervised learning tasks a...
In this article, we present an idea for solving deterministic partially observable markov decision processes (POMDPs) based on a history space containing sequences of past observat...