DocumentCode
1797696
Title
Approximate planning in POMDPs via MDP heuristic
Author
Yong Lin ; Xingjia Lu ; Makedon, Fillia
Author_Institution
Coll. of Sci., Ningbo Univ. of Technol., Ningbo, China
fYear
2014
fDate
6-11 July 2014
Firstpage
1304
Lastpage
1309
Abstract
MDP heuristic based POMDP algorithms have been considered as simple, fast, but imprecise solutions. This paper provides a novel MDP heuristic value iteration algorithm for POMDPs. Besides the help of MDP, our algorithm utilizes a weighted graph model for the belief point approximation and reassignment, to further improve the efficiency and decrease the space complexity. Experimental results indicate our algorithm is fast and has high solution quality for POMDP problems.
Keywords
Markov processes; approximation theory; computational complexity; graph theory; iterative methods; planning (artificial intelligence); MDP heuristic value iteration algorithm; POMDP; approximate planning; belief point approximation; belief point reassignment; partially observable Markov decision process; space complexity; weighted graph model; Approximation algorithms; Approximation methods; Convergence; Heuristic algorithms; Mathematical model; Planning; Trajectory;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks (IJCNN), 2014 International Joint Conference on
Conference_Location
Beijing
Print_ISBN
978-1-4799-6627-1
Type
conf
DOI
10.1109/IJCNN.2014.6889576
Filename
6889576
Link To Document