Decentralized partially observable MDPs (DEC-POMDPs) provide a rich framework for modeling decision making by a team of agents. Despite rapid progress in this area, the limited sc...
Distributed Partially Observable Markov Decision Problems (Distributed POMDPs) are evolving as a popular approach for modeling multiagent systems, and many different algorithms ha...
A problem of planning for cooperative teams under uncertainty is a crucial one in multiagent systems. Decentralized partially observable Markov decision processes (DECPOMDPs) prov...
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....
The problem of modeling a variety of domains within the framework of one general scheme is of central importance in AI. This paper presents the MultiEntity model for multi-agent p...