Using focal point learning to improve human-machine tacit coordination

We consider an automated agent that needs to coordinate with a human partner when communication between them is not possible or is undesirable (tacit coordination games). Specifically, we examine situations where an agent and human attempt to coordinate their choices among several alternatives with equivalent utilities. We use machine learning algorithms to help the agent predict human choices in these tacit coordination domains. Experiments have shown that humans are often able to coordinate with one another in communication-free games, by using focal points, “prominent” solutions to coordination problems. We integrate focal point rules into the machine learning process, by transforming raw domain data into a new hypothesis space. We present extensive empirical results from three different tacit coordination domains. The Focal Point Learning approach results in classifiers with a 40% to 80% higher correct classification rate, and shorter training time, than when using regular ...

• Type: Journal
• Year: 2011
• Where: AAMAS
• Authors: Inon Zuckerman, Sarit Kraus, Jeffrey S. Rosenschein
• Keywords: AAMAS 2011, Focal Point, Intelligent Agents, Tacit Coordination, Tacit Coordination Games