Using focal point learning to improve human-machine tacit coordination

Inon Zuckerman*, Sarit Kraus, Jeffrey S. Rosenschein

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

23 Scopus citations

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.

Original languageAmerican English
Pages (from-to)289-316
Number of pages28
JournalAutonomous Agents and Multi-Agent Systems
Volume22
Issue number2
DOIs
StatePublished - Mar 2011

Bibliographical note

Funding Information:
Acknowledgements This research is based upon work supported in part by the U.S. Army Research Laboratory and the U.S. Army Research Office under grant number W911NF-08-1-0144, AFOSR grant FA95500610405, NSF grant 0705587 and under ISF grant #1357/07. We appreciate the comments of the anonymous reviewers, which were quite useful.

Keywords

  • Autonomous agents
  • Cognitive model
  • Focal points
  • Human-machine interaction
  • Tactic coordination

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