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...
The Partially Observable Markov Decision Process has long been recognized as a rich framework for real-world planning and control problems, especially in robotics. However exact s...
Joelle Pineau, Geoffrey J. Gordon, Sebastian Thrun
In this article, we present an idea for solving deterministic partially observable markov decision processes (POMDPs) based on a history space containing sequences of past observat...
Given a model of a physical process and a sequence of commands and observations received over time, the task of an autonomous controller is to determine the likely states of the p...
Abstract— We propose a planning algorithm that allows usersupplied domain knowledge to be exploited in the synthesis of information feedback policies for systems modeled as parti...
Salvatore Candido, James C. Davidson, Seth Hutchin...