Evolutionary testing of autonomous software agents

A system built in terms of autonomous agents may require even greater correctness assurance than one which 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 optimization to generate demanding test cases. Categories and Subject Descriptors D.2.5 [Software Engineering]: Testing and Debugging General...

• Type: Conference
• Year: 2009
• Where: ATAL
• Authors: Cu D. Nguyen, Anna Perini, Paolo Tonella, Simon Miles, Mark Harman, Michael Luck
• Keywords: Artificial Intelligence, ATAL 2009, Autonomous Agents, Test Cases, Testing Autonomous Agents