Why AM an EUISKO appear to work.
Artificial Intelligence
Evolving adaptive pheromone path planning mechanisms
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Using genetic algorithms for early schedulability analysis and stress testing in real-time systems
Genetic Programming and Evolvable Machines
Testing the limits of emergent behavior in MAS using learning of cooperative behavior
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Testing of precision agricultural networks for adversary-induced problems
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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We present a general scheme for testing multi-agent systems, respectively policies used by them, for unwanted emergent behavior using learning of cooperative behavior via particle swarm systems. By using particle swarm systems in this setting, we are able to create agents interacting/attacking the tested agents that can use parameterised high-level actions. We also can evaluate the quality of an attack using several measures that can be prioritised and used in a multi-objective manner in the search. This solves some general problems of other testing approaches using learning. We instantiate this general scheme to test harbour patrol and interception policies for two Canadian harbours, showing that our approach is able to find problems in these policies.