We propose a novel approach to optimize Partially Observable Markov Decisions Processes (POMDPs) defined on continuous spaces. To date, most algorithms for model-based POMDPs are ...
Josep M. Porta, Nikos A. Vlassis, Matthijs T. J. S...
This paper presents the CQ algorithm which decomposes and solves a Markov Decision Process (MDP) by automatically generating a hierarchy of smaller MDPs using state variables. The ...
The ability for an agent to reason under uncertainty is crucial for many planning applications, since an agent rarely has access to complete, error-free information about its envi...
Multi-agent systems are an increasingly important software paradigm and in many of its applications agents cooperate to achieve a particular goal. This requires the design of effi...
Taolue Chen, Marta Z. Kwiatkowska, David Parker, A...
Coreference analysis, also known as record linkage or identity uncertainty, is a difficult and important problem in natural language processing, databases, citation matching and ...