Partially Observable Markov Decision Processes (POMDPs) provide a rich framework for sequential decision-making under uncertainty in stochastic domains. However, solving a POMDP i...
In this paper, we present an online method for POMDPs, called RTBSS (Real-Time Belief Space Search), which is based on a look-ahead search to find the best action to execute at e...
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...
In this paper, we present a Dynamic Load Balancing (DLB) policy for problems characterized by a highly irregular search tree, whereby no reliable workload prediction is available....
Decentralized partially observable Markov decision processes (DEC-POMDPs) form a general framework for planning for groups of cooperating agents that inhabit a stochastic and part...
Matthijs T. J. Spaan, Geoffrey J. Gordon, Nikos A....