Using Focal Point Learning to Improve Tactic Coordination in Human-Machine Interactions
Inon Zuckerman, Sarit Kraus and Jeffrey S. Rosenschein
In: IJCAI 2007, January 2007, Hyderabad, India.
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.