The hidden dangers of experimenting in distributed AI
conference paper
Research on multi-agent systems often involves experiments, also in situations where humans interact with agents. Consequently, the field of experimental (human) sciences becomes more and more relevant. This paper clarifies how things can and often do go wrong in distributed AI experiments. We show the flaws in methodological design in existing papers (both with and without humans) and work out an example involving human test-subjects to introduce the fundamental issues of experimental design. Furthermore, we provide researchers with an approach to improve their experimental design. We wish to stimulate researchers to conduct better experiments – which will benefit us all.
TNO Identifier
224330
Source title
Fifth International Joint Conference on Autonomous Agents and Multiagent Systems - AMAS-06, 8-12 May 2006, Hakodate, Japan
Pages
1320-1322
Files
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