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Proactive communication in multi-agent teamwork

Sharing common goals and acting cooperatively are critical issues in multiagent
teamwork. Traditionally, agents cooperate with each other by inferring others'
actions implicitly or explicitly, based on established norms for behavior or on
knowledge about the preferences or interests of others. This kind of cooperation either
requires that agents share a large amount of knowledge about the teamwork, which is
unrealistic in a distributed team, or requires high-frequency message exchange, which
weakens teamwork efficiency, especially for a team that may involve human members.
In this research, we designed and developed a new approach called Proactive
Communication, which helps to produce realistic behavior and interactions for multiagent
teamwork. We emphasize that multi-agent teamwork is governed by the same
principles that underlie human cooperation. Psychological studies of human teamwork
have shown that members of an effective team often anticipate the needs of other
members and choose to assist them proactively. Human team members are also
naturally capable of observing the environment and others so they can establish certain
parameters for performing actions without communicating with others. Proactive
Communication endows agents with observabilities and enables agents use them to
track others’ mental states. Additionally, Proactive Communication uses statistical analysis of the information production and need of team members and uses these data
to capture the complex, interdependent decision processes between information needer
and provider. Since not all these data are known, we use their expected values with
respect to a dynamic estimation of distributions.
The approach was evaluated by running several sets of experiments on a Multi-
Agent Wumpus World application. The results showed that endowing agents with
observability decreased communication load as well as enhanced team performance.
The results also showed that with the support of dynamic distributions, estimation, and
decision-theoretic modeling, teamwork efficiency were improved.
Date25 April 2007
CreatorsZhang, Yu
ContributorsVolz, Richard A.
PublisherTexas A&M University
Source SetsTexas A and M University
Detected LanguageEnglish
TypeBook, Thesis, Electronic Dissertation, text
Format1030297 bytes, electronic, application/pdf, born digital

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