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Evolutionary testing of autonomous software agents

Research output: Contribution to journalArticle

Cu D. Nguyen, Simon Miles, Anna Perini, Paolo Tonella, Mark Harman, Michael Luck

Original languageEnglish
Pages (from-to)260 - 283
Number of pages24
JournalAutonomous Agents and Multi-Agent Systems
Issue number2
StatePublished - Sep 2012


King's Authors


A system built in terms of autonomous software agents may require even greater correctness assurance than one that is merely reacting to the immediate control of its users. Agents make substantial decisions for themselves, so thorough testing is an important consideration. However, autonomy also makes testing harder; by their nature, autonomous agents may react in different ways to the same inputs over time, because, for instance they have changeable goals and knowledge. For this reason, we argue that testing of autonomous agents requires a procedure that caters for a wide range of test case contexts, and that can search for the most demanding of these test cases, even when they are not apparent to the agents' developers. In this paper, we address this problem, introducing and evaluating an approach to testing autonomous agents that uses evolutionary optimisation to generate demanding test cases. We propose a methodology to derive objective (fitness) functions that drive evolutionary algorithms, and evaluate the overall approach with two simulated autonomous agents. The obtained results show that our approach is effective in finding good test cases automatically.

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