A study on two measurements-to-tracks data assignment algorithms
Information Sciences: an International Journal
Online optimization of replacement policies using learning automata
International Journal of Systems Science
Knowledge discovery and emergent complexity in bioinformatics
KDECB'06 Proceedings of the 1st international conference on Knowledge discovery and emergent complexity in bioinformatics
Analyzing stigmergetic algorithms through automata games
KDECB'06 Proceedings of the 1st international conference on Knowledge discovery and emergent complexity in bioinformatics
Analyzing the dynamics of stigmergetic interactions through pheromone games
Theoretical Computer Science
A biologically inspired sensor wakeup control method for wireless sensor networks
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Speeding up learning automata based multi agent systems using the concepts of stigmergy and entropy
Expert Systems with Applications: An International Journal
A tree-growth based ant colony algorithm for QoS multicast routing problem
Expert Systems with Applications: An International Journal
Bearings-only target tracking using node selection based on an accelerated ant colony optimization
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part II
Learning automata-based approach to learn dialogue policies in large state space
International Journal of Intelligent Information and Database Systems
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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.