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Role-Based Ad Hoc Teamwork.
Katie Genter, Noa
Agmon, and Peter Stone.
In Gita Sukthankar, Robert P. Goldman, Christopher
Geib, David V. Pyhadath, and Hung Hai Bui, editors, Plan, Activity, and Intent Recognition: Theory and Practice, pp.
251–272, Elsevier, Philadelphia, PA, USA, 2013.
An ad hoc team setting is one in which teammates work together to obtain a common goal, but without any prior agreement regarding how to work together. We introduce a role-based approach for ad hoc teamwork, in which each teammate is inferred to be following a specialized role. In such cases, the role an ad hoc agent should select depends on its own capabilities and on the roles selected by its teammates. In this chapter we formally define methods for evaluating the influence of an ad hoc agent's role selection on the team's utility and show that use of these methods facilitates efficient calculation of the role yielding maximal team utility. We examine empirically how to choose the best suited method for role assignment and show that once an appropriate assignment method is determined for a domain, it can be used successfully in new tasks that the team has not encountered before. Unlike much of the rest of the book, this chapter does not focus on methods for recognizing the roles of the other agents. Rather, it examines the question of how to use successful role recognition towards successful multiagent decision-making.
@InCollection{PAIR13-katie,
author = {Katie Genter and Noa Agmon and Peter Stone},
title = {Role-Based Ad Hoc Teamwork},
editor = {Gita Sukthankar and Robert P. Goldman and Christopher Geib and David V. Pyhadath and Hung Hai Bui},
booktitle = {Plan, Activity, and Intent Recognition: Theory and Practice},
publisher = {Elsevier},
address = {Philadelphia, PA, USA},
year = {2013},
pages= {251--272},
abstract = {An ad hoc team setting is one in which teammates work together to
obtain a common goal, but without any prior agreement regarding how to work
together. We introduce a role-based approach for ad hoc teamwork, in which
each teammate is inferred to be following a specialized role. In such cases,
the role an ad hoc agent should select depends on its own capabilities and on
the roles selected by its teammates. In this chapter we formally define
methods for evaluating the influence of an ad hoc agent's role selection on
the team's utility and show that use of these methods facilitates efficient
calculation of the role yielding maximal team utility. We examine
empirically how to choose the best suited method for role assignment and show
that once an appropriate assignment method is determined for a domain, it can
be used successfully in new tasks that the team has not encountered before.
Unlike much of the rest of the book, this chapter does not focus on methods
for recognizing the roles of the other agents. Rather, it examines the
question of how to use successful role recognition towards successful
multiagent decision-making.},
}
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