Paper Info
Title
An online POMDP algorithm for complex multiagent environments
Abstract
In this paper, we present an online method for POMDPs, called RTBSS (Real-Time Belief Space Search), which is based on a look-ahead search to find the best action to execute at each cycle in an environment. We thus avoid the overwhelming complexity of computing a policy for each possible situation. By doing so, we show that this method is particularly efficient for large real-time environments where offline approaches are not applicable because of their complexity. We first describe the formalism of our online method, followed by some results on standard POMDPs. Then, we present an adaptation of our method for a complex multiagent environment and results showing its efficiency in such environments.
Year
DOI
Venue
2005
10.1145/1082473.1082620
AAMAS
Keywords
Field
DocType
offline approach,best action,real-time belief space search,online method,online pomdp algorithm,possible situation,standard pomdps,overwhelming complexity,look-ahead search,large real-time environment,complex multiagent environment,online search,look ahead,pomdp,real time
Computer science,Partially observable Markov decision process,Artificial intelligence,Formalism (philosophy),Machine learning,Online search
Conference
ISBN
Citations 
PageRank 
1-59593-093-0
40
2.31
References 
Authors
16
3
Name
Order
Citations
PageRank
Sébastien Paquet125317.43
Ludovic Tobin2574.22
Chaib-draa, Brahim31190113.23