Learning team coordination constraints through execution
Modi, P.J.; Shen, W.-M.
MultiAgent Systems, 2000. Proceedings. Fourth International Conference on
Volume , Issue , 2000 Page(s):417 - 418
Digital Object Identifier 10.1109/ICMAS.2000.858503
Summary:Agents working together in teams can tackle user-defined tasks
more complex than those they can perform as individuals. However
constructing such teams remains a difficult challenge. In particular
current approaches to designing agent teams are highly labor-intensive.
Human designers must deal with overwhelming complexity in trying to
manage the large number of interactions and dependencies that may exist
between agent activities. Even if the designer is able to come up with a
plan that seems to work, he cannot be sure that it will continue to work
in all possible situations. We propose to use machine learning
techniques to assist a user in building robust, multiagent team plans.
This is done by logging information during team plan executions and
attempting to find the cause of failure from this data. We present a
method for learning temporal coordination constraints on actions in a
multiagent reactive plan. We also briefly discuss the effect of new
coordination constraints on team organization
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