Markov decision processes (MDPs) are controllable discrete event systems with stochastic transitions. Performances of an MDP are evaluated by a payoff function. The controller of ...
The POMDP is considered as a powerful model for planning under uncertainty. However, it is usually impractical to employ a POMDP with exact parameters to model precisely the real-...
The advent of Web services has made automated workflow composition relevant to Web based applications. One technique that has received some attention, for automatically composing ...
Prashant Doshi, Richard Goodwin, Rama Akkiraju, Ku...
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 present two new algorithms for finding optimal strategies for discounted, infinite-horizon, Deterministic Markov Decision Processes (DMDP). The first one is an adaptation of...