Author:
Gavin Rens
Affiliation:
University of KwaZulu-Natal and CSIR Meraka, South Africa
Keyword(s):
Online, POMDP, Planning, Heuristic, Optimization, Belief-state Compression, Expected Feature Values.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Formal Methods
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Planning and Scheduling
;
Reactive AI
;
Simulation and Modeling
;
Soft Computing
;
Symbolic Systems
;
Uncertainty in AI
Abstract:
A novel algorithm to speed up online planning in partially observable Markov decision processes (POMDPs) is
introduced. I propose a method for compressing nodes in belief-decision-trees while planning occurs. Whereas
belief-decision-trees branch on actions and observations, with my method, they branch only on actions. This is
achieved by unifying the branches required due to the nondeterminism of observations. The method is based
on the expected values of domain features. The new algorithm is experimentally compared to three other
online POMDP algorithms, outperforming them on the given test domain.