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Goal-Converging Behavior Networks and Self-Solving Planning Domains
Computer Science Faculty Proceedings & Presentations
  • Bernhard Nebel, Universitat fur Informatik
  • Yuliya Lierler, University of Nebraska at Omaha
Document Type
Conference Proceeding
Publication Date
Agents operating in the real world have to deal with a constantly changing and only partially predictable environment and are nevertheless expected to choose reasonable actions quickly. One way to address this problem is to use behavior networks as proposed by Maes, which support real-time decision making. Robotic soccer appears to be one domain where behavior networks have been proven to be particularly successful. In this paper, we analyze the reason for the success by identifying conditions that make behavior networks goal converging, i.e., allow them to reach the goals regardless of which particular action selection scheme is used. In terms of STRIPS domains one could talk of self-solving planning domains. We finally show that the behavior networks used for different robotic soccer teams have this property.

IJCAI-03 Workshop on Agents in Real-time and Dynamic Environments

Citation Information
Bernhard Nebel and Yuliya Lierler. "Goal-Converging Behavior Networks and Self-Solving Planning Domains" (2003)
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