Learning automata: an introduction
Learning automata: an introduction
Traderbots: a new paradigm for robust and efficient multirobot coordination in dynamic environments
Traderbots: a new paradigm for robust and efficient multirobot coordination in dynamic environments
A Distributed Bidirectional Auction Algorithm for Multirobot Coordination
ICRCCS '09 Proceedings of the 2009 International Conference on Research Challenges in Computer Science
CoMutaR: a framework for multi-robot coordination and task allocation
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation - 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
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
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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, as opposed to the usual multi-tasks allocation problem in multi-robot systems in which an external controller distributes the existing tasks among the individual robots. In this work we are considering a specifically distributed or decentralized approach in which 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 and we propose a solution through automata learning-based probabilistic algorithm, to solve the corresponding multi-tasks distribution problem. The paper ends with a critical discussion of experimental results.