Cooperative multiagent robotic systems
Artificial intelligence and mobile robots
Coordination and Learning in Multirobot Systems
IEEE Intelligent Systems
Genetic Algorithms for the Traveling Salesman Problem
Proceedings of the 1st International Conference on Genetic Algorithms
On the Definition and Representation of a Ranking
ReIMICS '01 Revised Papers from the 6th International Conference and 1st Workshop of COST Action 274 TARSKI on Relational Methods in Computer Science
Multi-agent learning of heterogeneous robots by evolutionary subsumption
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
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Using artificial evolution successfully to design behaviours of multiple robot systems has been reported in recent years. Most of such reports are focused on the design of low level controllers. Design of high level team coordination strategies is rarely covered perhaps because the design of an appropriate chromosome representation for a complex multi-agent system is not an easy task. In this paper we propose that by treating the action decisions of every team member as a supervised ranking problem, the chromosome design problem can be solved systematically.We have tested this approach by dynamically solving the problems in the Solomon's benchmark of Vehicle Routing Problem with Time Windows [1]. Experiments show that our approach can create some simple behaviours which, whilst not optimal, are robust and above average in quality.