Abstract— This paper proposes a simulation-based active policy learning algorithm for finite-horizon, partially-observed sequential decision processes. The algorithm is tested i...
Ruben Martinez-Cantin, Nando de Freitas, Arnaud Do...
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....
Planning in large, partially observable domains is challenging, especially when a long-horizon lookahead is necessary to obtain a good policy. Traditional POMDP planners that plan...
This paper presents a systems analysis perspective that extends the traditional process design framework to green process design, green energy and industrial ecology leading to su...
We investigate in this paper the anchor selection problem with consideration of anchor location uncertainty, which arises naturally from error propagation problem in successive or...