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Using focal point learning to improve human–machine tacit coordination

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Abstract

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 40–80% higher correct classification rate, and shorter training time, than when using regular classifiers, and a 35% higher correct classification rate than classical focal point techniques without learning. In addition, the integration of focal points into learning algorithms results in agents that are more robust to changes in the environment. We also present several results describing various biases that might arise in Focal Point based coordination.

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Authors and Affiliations

  1. The Institute for Advanced Computer Studies, University of Maryland, College Park, MD, 20742, USA

    Inon Zuckerman & Sarit Kraus

  2. The School of Engineering and Computer Science, The Hebrew University, Jerusalem, Israel

    Jeffrey S. Rosenschein

Authors
  1. Inon Zuckerman
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  2. Sarit Kraus
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  3. Jeffrey S. Rosenschein
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Corresponding author

Correspondence to Inon Zuckerman.

Additional information

A preliminary version of this article appeared in the Proceedings of the Twentieth International Joint Conference on Artificial Intelligence (IJCAI 2007).

Inon Zuckerman—The research was done as part of the author’s PhD research in the Department of Computer Science, Bar-Ilan University, Ramat-Gan, Israel.

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Zuckerman, I., Kraus, S. & Rosenschein, J.S. Using focal point learning to improve human–machine tacit coordination. Auton Agent Multi-Agent Syst 22, 289–316 (2011). https://doi.org/10.1007/s10458-010-9126-5

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