Learning automata: an introduction
Learning automata: an introduction
Ant colony optimization theory: a survey
Theoretical Computer Science
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation - Volume Part I
MICAI'11 Proceedings of the 10th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
Guest editorial learning automata: theory, paradigms, and applications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Multirobot systems: a classification focused on coordination
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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This paper focuses on the general problem of coordinating multiple robots. More specifically, it addresses the self-election of heterogeneous specialized tasks by autonomous robots. In this paper we focus on a specifically distributed or decentralized approach as we are particularly interested on decentralized solution where the robots themselves autonomously and in an individual manner, are responsible of selecting a particular task so that all the existing tasks are optimally distributed and executed. In this regard, we have established an experimental scenario to solve the corresponding multi-tasks distribution problem and we propose a solution using two different approaches by applying Ant Colony Optimization-based deterministic algorithms as well as Learning Automata-based probabilistic algorithms. We have evaluated the robustness of the algorithm, perturbing the number of pending loads to simulate the robot's error in estimating the real number of pending tasks and also the dynamic generation of loads through time. The paper ends with a critical discussion of experimental results.