Distributed Partially Observable Markov Decision Problems (Distributed POMDPs) are a popular approach for modeling multi-agent systems acting in uncertain domains. Given the signi...
Pradeep Varakantham, Janusz Marecki, Yuichi Yabu, ...
Decentralized planning in uncertain environments is a complex task generally dealt with by using a decision-theoretic approach, mainly through the framework of Decentralized Parti...
Multiagent Partially Observable Markov Decision Processes are a popular model of multiagent systems with uncertainty. Since the computational cost for finding an optimal joint pol...
Many sectors of the military are interested in Self-Organized (SO) systems because of their flexibility, versatility and economics. The military is researching and employing auto...
Dustin J. Nowak, Gary B. Lamont, Gilbert L. Peters...
This paper extends the framework of partially observable Markov decision processes (POMDPs) to multi-agent settings by incorporating the notion of agent models into the state spac...