Testing the limits of emergent behavior in MAS using learning of cooperative behavior

Authors:
Jordan Kidney;Jörg Denzinger
Affiliations:
Department of Computer Science, University of Calgary, Canada, email: {kidney,denzinge}@cpsc.ucalgary.ca;Department of Computer Science, University of Calgary, Canada, email: {kidney,denzinge}@cpsc.ucalgary.ca
Venue:
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Year:
2006

Citing 4
Cited 5

Predicting dependability by testing

ISSTA '96 Proceedings of the 1996 ACM SIGSOFT international symposium on Software testing and analysis
Visualising and debugging distributed multi-agent systems

Proceedings of the third annual conference on Autonomous Agents
Debugging multi-agent systems using design artifacts: the case of interaction protocols

Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
Evolutionary On-line Learning of Cooperative Behavior with Situation-Action-Pairs

ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)

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

IAT '06 Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology
Tools for analyzing intelligent agent systems

Web Intelligence and Agent Systems
Improving testing of multi-unit computer players for unwanted behavior using coordination macros

CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Testing harbour patrol and interception policies using particle-swarm-based learning of cooperative behavior

CISDA'09 Proceedings of the Second IEEE international conference on Computational intelligence for security and defense applications
Testing in multi-agent systems

AOSE'10 Proceedings of the 10th international conference on Agent-oriented software engineering

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present a method to test a group of agents for (unwanted) emergent behavior by using techniques from learning of cooperative behavior. The general idea is to mimick users or other systems interacting with the tested agents by a group of tester agents and to evolve the actions of these tester agents. The goal of this evolutionary learning is to get the tested agents to exhibit the (unwanted) emergent behavior. We used our method to test the emergent properties of a team of agents written by students for an assignment in a basic MAS class. Our method produced tester agents that helped the tested agents to perform at their best and another configuration of our system showed how much the tested agents could hold their own against very competitive agents, which revealed breakdowns in the tested agents' cooperation.