Using focal point learning to improve tactic coordination in human-machine interactions

Inon Zuckerman, Sarit Kraus, Jeffrey S. Rosenschein

Research output: Contribution to journalConference articlepeer-review

5 Scopus citations


We consider an automated agent that needs to coordinate with a human partner when communication between them is not possible or is undesirable (tactic 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 tactic coordination domains. Learning to classify general human choices, however, is very difficult. Nevertheless, 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 points into the machine learning process, by transforming raw domain data into a new hypothesis space. This results in classifiers with an improved classification rate and shorter training time. Integration of focal points into learning algorithms also results in agents that are more robust to changes in the environment.

Original languageEnglish
Pages (from-to)1563-1568
Number of pages6
JournalIJCAI International Joint Conference on Artificial Intelligence
StatePublished - 2007
Externally publishedYes
Event20th International Joint Conference on Artificial Intelligence, IJCAI 2007 - Hyderabad, India
Duration: 6 Jan 200712 Jan 2007


Dive into the research topics of 'Using focal point learning to improve tactic coordination in human-machine interactions'. Together they form a unique fingerprint.

Cite this