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Patrick Riley and Manuela Veloso. Planning
for Distributed Execution Through Use of Probabilistic Opponent Models. In Proceedings of the Sixth International
Conference on AI Planning and Scheduling (AIPS-2002), pp. 72–81, 2002. Best Paper Award
Publisher (AAAI) Website
[PDF]136.3kB [gzipped postscript]195.9kB
In multiagent domains with adversarial and cooperative team agents, team agents should be adaptive to the current environment and opponent. We introduce an online method to provide the agents with team plans that a ``coach'' agent generates in response to the specific opponents. The coach agent can observe the agents' behaviors but it has only periodic communication with the rest of the team. The coach uses a Simple Temporal Network to represent team plans as coordinated movements among the multiple agents and the coach searches for an opponent-dependent plan for its teammates. This plan is then communicated to the agents, who execute the plan in a distributed fashion, using information from the plan to maintain consistency among the team members. In order for these plans to be effective and adaptive, models of opponent movement are used in the planning. The coach is then able to quickly select between different models online by using a Bayesian style update on a probability distribution over the models. Planning then uses the model which is found to be the most likely. The system is fully implemented in a simulated robotic soccer environment. In several recent games with completely unknown adversarial teams, the approach demonstrated a visible adaptation to the different teams.
@InProceedings{AIPS02-STN,
author = {Patrick Riley and Manuela Veloso},
title = {Planning for Distributed Execution Through Use of
Probabilistic Opponent Models},
booktitle = aips2002,
year = 2002,
pages = {72--81},
note = {{\it Best Paper Award}},
wwwnote = {<a href="http://www.aaai.org">Publisher (AAAI) Website</a>},
abstract = { In multiagent domains with adversarial and
cooperative team agents, team agents should be
adaptive to the current environment and opponent. We
introduce an online method to provide the agents
with team plans that a ``coach'' agent generates in
response to the specific opponents. The coach agent
can observe the agents' behaviors but it has only
periodic communication with the rest of the
team. The coach uses a Simple Temporal Network to
represent team plans as coordinated movements among
the multiple agents and the coach searches for an
opponent-dependent plan for its teammates. This plan
is then communicated to the agents, who execute the
plan in a distributed fashion, using information
from the plan to maintain consistency among the team
members. In order for these plans to be effective
and adaptive, models of opponent movement are used
in the planning. The coach is then able to quickly
select between different models online by using a
Bayesian style update on a probability distribution
over the models. Planning then uses the model which
is found to be the most likely. The system is fully
implemented in a simulated robotic soccer
environment. In several recent games with completely
unknown adversarial teams, the approach demonstrated
a visible adaptation to the different teams. },
bib2html_pubtype = {Refereed Conference,Award Winner},
bib2html_rescat = {Planning,Coaching},
bib2html_funding = {NSF,CoABS,ActiveTemplates},
}
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