Partially observable Markov decision processes (POMDPs) allow one to model complex dynamic decision or control problems that include both action outcome uncertainty and imperfect ...
Learning graphical models with hidden variables can offer semantic insights to complex data and lead to salient structured predictors without relying on expensive, sometime unatta...
We study observation-based strategies for partially-observable Markov decision processes (POMDPs) with parity objectives. An observationbased strategy relies on partial information...
Krishnendu Chatterjee, Laurent Doyen, Thomas A. He...
We examine the computational complexity of testing and nding small plans in probabilistic planning domains with both at and propositional representations. The complexity of plan e...
Michael L. Littman, Judy Goldsmith, Martin Mundhen...
We adopt the decision-theoretic principle of expected utility maximization as a paradigm for designing autonomous rational agents operating in multi-agent environments. We use the...