DocumentCode :
870025
Title :
Colonies of learning automata
Author :
Verbeeck, Katja ; Nowé, Ann
Author_Institution :
Computational Modeling Lab., Vrije Univ., Brussels, Belgium
Volume :
32
Issue :
6
fYear :
2002
fDate :
12/1/2002 12:00:00 AM
Firstpage :
772
Lastpage :
780
Abstract :
Originally, learning automata (LAs) were introduced to describe human behavior from both a biological and psychological point of view. In this paper, we show that a set of interconnected LAs is also able to describe the behavior of an ant colony, capable of finding the shortest path from their nest to food sources and back. The field of ant colony optimization (ACO) models ant colony behavior using artificial ant algorithms. These algorithms find applications in a whole range of optimization problems and have been experimentally proved to work very well. It turns out that a known model of interconnected LA, used to control Markovian decision problems (MDPs) in a decentralized fashion, matches perfectly with these ant algorithms. The field of LAs can thus both impart in the understanding of why ant algorithms work so well and may also become an important theoretical tool for learning in multiagent systems (MAS) in general. To illustrate this, we give an example of how LAs can be used directly in common Markov game problems.
Keywords :
Markov processes; decision theory; game theory; learning automata; multi-agent systems; optimisation; Markov game problems; Markovian decision problems; ant colony optimization; artificial ant algorithms; interconnected learning automata; learning automata colonies; multiagent systems; shortest path; Ant colony optimization; Communication industry; Convergence; Humans; Learning automata; Manufacturing industries; Monitoring; Multiagent systems; Psychology; Telecommunication network management;
fLanguage :
English
Journal_Title :
Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on
Publisher :
ieee
ISSN :
1083-4419
Type :
jour
DOI :
10.1109/TSMCB.2002.1049611
Filename :
1049611
Link To Document :
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