Recent developments in grid-based and point-based approximation algorithms for POMDPs have greatly improved the tractability of POMDP planning. These approaches operate on sets of...
Joelle Pineau, Geoffrey J. Gordon, Sebastian Thrun
Predictive state representation (PSR) models for controlled dynamical systems have recently been proposed as an alternative to traditional models such as partially observable Mark...
Michael R. James, Satinder P. Singh, Michael L. Li...
Partially Observable Markov Decision Process (POMDP) is a popular framework for planning under uncertainty in partially observable domains. Yet, the POMDP model is riskneutral in ...
Although a partially observable Markov decision process (POMDP) provides an appealing model for problems of planning under uncertainty, exact algorithms for POMDPs are intractable...
Distributed POMDPs provide an expressive framework for modeling multiagent collaboration problems, but NEXPComplete complexity hinders their scalability and application in real-wo...
Pradeep Varakantham, Jun-young Kwak, Matthew E. Ta...