In the past, Markov Decision Processes (MDPs) have become a standard for solving problems of sequential decision under uncertainty. The usual request in this framework is the compu...
Partially observable Markov decision processes (POMDPs) provide an elegant mathematical framework for modeling complex decision and planning problems in stochastic domains in whic...
Recent scaling up of decentralized partially observable Markov decision process (DEC-POMDP) solvers towards realistic applications is mainly due to approximate methods. Of this fa...
1 We consider the problem of scheduling an unknown sequence of tasks for a single server as the tasks arrive with the goal off maximizing the total weighted value of the tasks serv...
We present a heuristic search algorithm for solving first-order Markov Decision Processes (FOMDPs). Our approach combines first-order state abstraction that avoids evaluating stat...