Name
Abstract | ||
|---|---|---|
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 re- act in dierent 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 demand- ing 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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1145/1558013.1558085 | Autonomous Agents & Multiagent Systems/Agent Theories, Architectures, and Languages |
Keywords | DocType | Volume |
Testing autonomous agents,Evolutionary testing,Quality requirements | Conference | 25 |
Issue | Citations | PageRank |
2 | 29 | 1.22 |
References | Authors | |
21 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cu D. Nguyen | 1 | 224 | 14.19 |
| Anna Perini | 2 | 1165 | 83.51 |
| Paolo Tonella | 3 | 3559 | 224.88 |
| Simon Miles | 4 | 1599 | 109.29 |
| Mark Harman | 5 | 10264 | 389.82 |
| Michael Luck | 6 | 3440 | 275.97 |