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Building agent teams using an explicit teamwork model and learning
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AI
DOI：10.1016/S0004-3702(99)00022-3.png)
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摘要
En 中文
Multi-agent collaboration or teamwork and learning are two critical research challenges in a large number of multi-agent applications. These research challenges are highlighted in RoboCup, an international project focused on robotic and synthetic soccer as a common testbed for research in multi-agent systems. This article describes our approach to address these challenges, based on a team of soccer-playing agents built for the simulation league of RoboCup-the most popular of the RoboCup leagues so far. To address the challenge of teamwork, we investigate a novel approach based on the (re)use of a domain-independent, explicit model of teamwork, an explicitly represented hierarchy of team plans and goals, and a team organization hierarchy based on roles and role-relationships. This general approach to teamwork, shown to be applicable in other domains beyond RoboCup, both reduces development time and improves teamwork flexibility. We also demonstrate the application of off-line and on-line learning to improve and specialize agents' individual skills in RoboCup. These capabilities enabled our soccer-playing team, ISIS, to successfully participate in the first international RoboCup soccer tournament (RoboCup'97) held in Nagoya, Japan, in August 1997. ISIS won the third-place prize in over 30 teams that participated in the simulation league. (C) 1999 Elsevier Science B.V. All rights reserved.
Keyword:
multi-agents
teamwork
agent learning
RoboCup soccer
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