Learning automata: an introduction
Learning automata: an introduction
Using Finite State Automata to Produce Self-Optimization and Self-Control
IEEE Transactions on Parallel and Distributed Systems
Learning Algorithms Theory and Applications
Learning Algorithms Theory and Applications
Learning Automata and Stochastic Optimization
Learning Automata and Stochastic Optimization
Cooperative Mobile Robotics: Antecedents and Directions
Autonomous Robots
New search algorithm for randomly located objects: a non-cooperative agent based approach
CISDA'09 Proceedings of the Second IEEE international conference on Computational intelligence for security and defense applications
Particle swarm optimization using lévy probability distribution
ISICA'07 Proceedings of the 2nd international conference on Advances in computation and intelligence
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In this paper we address the general question of what is the best strategy to search efficiently for randomly located objects (target sites). We propose a new agent based algorithm for searching in an unpredictable environment. The originality of our work consists in applying a non-cooperative strategy, namely the distributed Goore Game model, as opposed to applying the classical collaborative and competitive strategies, or individual strategies. This paper covers only the destructive search that occurs when the agent visits the target only one time. The proposed algorithm has two versions: one when the agent can move with a step equal to unity and the other when the step of the agent follows a Levy flight distribution. The latter version is inspired by the work of A.M. Reynolds, motivated by biological examples.