Evaluating Different Genetic Operators in the Testing for Unwanted Emergent Behavior Using Evolutionary Learning of Behavior

We present an experimental comparison of different genetic operators regarding their use in an evolutionary learning method that searches for unwanted emergent behavior in a multi-agent system. The idea of the learning method is to evolve cooperative behavior of a group of so-called attack agents that act in the same environment as the tested agents. The attack agents use action sequences as agent architecture and the quality of a group of such agents is measured by how near their behavior brings the tested agents to show the unwanted behavior. Our experiments within the ARES II rescue simulator with an agent team written by students show that this method is able to find unwanted emergent behavior of the agents. They also show that rather standard genetic operators (on the team level and the agent level) are already sufficient to find this unwanted behavior.

[1]  Jörg Denzinger,et al.  Testing the Limits of Emergent Behavior in MAS Using Learning of Cooperative Behavior , 2006, ECAI.

[2]  Michael Winikoff,et al.  Debugging multi-agent systems using design artifacts: the case of interaction protocols , 2002, AAMAS '02.

[3]  S. Luke,et al.  A Comparison of Crossover and Mutation in Genetic Programming , 1997 .

[4]  Cem Kaner,et al.  Lessons Learned in Software Testing , 2001 .

[5]  Barbara Dunin-Keplicz,et al.  Proceedings of the 2005 IEEE/WIC/ACM International Conference on Intelligent Agent Technology , 2005 .

[6]  Jörg Denzinger,et al.  Evolutionary behavior testing of commercial computer games , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[7]  K.A. De Jong,et al.  Adaptive testing of controllers for autonomous vehicles , 1992, Proceedings of the 1992 Symposium on Autonomous Underwater Vehicle Technology.

[8]  Richard G. Hamlet,et al.  Predicting dependability by testing , 1996, ISSTA '96.

[9]  Xin Yao,et al.  An empirical study of genetic operators in genetic algorithms , 1993, Microprocess. Microprogramming.

[10]  Joachim Wegener,et al.  Evolutionary Testing of Embedded Systems , 2003 .

[11]  S. Luke,et al.  Collaborative Multiagent Learning : A Survey , 2003 .

[12]  Hyacinth S. Nwana,et al.  Visualising and debugging distributed multi-agent systems , 1999, AGENTS '99.

[13]  Jörg Denzinger,et al.  Teaching Multi-Agent Systems using the ARES Simulator , 2005 .