The design of inference algorithms for discrete-valued Markov Random Fields constitutes an ongoing research topic in computer vision. Large state-spaces, none-submodular energy-fun...
Existing controller-based approaches for centralized and decentralized POMDPs are based on automata with output known as Moore machines. In this paper, we show that several advant...
Decentralized POMDPs provide an expressive framework for sequential multi-agent decision making. Despite their high complexity, there has been significant progress in scaling up e...
Decentralized decision making under uncertainty has been shown to be intractable when each agent has different partial information about the domain. Thus, improving the applicabil...
Coordination of multiple agents under uncertainty in the decentralized POMDP model is known to be NEXP-complete, even when the agents have a joint set of goals. Nevertheless, we s...