A philosophical basis for knowledge acquisition
Knowledge Acquisition
RoboCup: The Robot World Cup Initiative
AGENTS '97 Proceedings of the first international conference on Autonomous agents
Incremental acquisition of search knowledge
International Journal of Human-Computer Studies
Generalising Ripple-Down Rules (Short Paper)
EKAW '00 Proceedings of the 12th European Workshop on Knowledge Acquisition, Modeling and Management
RoboCup 2001: Robot Soccer World Cup V
A User Oriented System for Developing Behavior Based Agents
RoboCup-98: Robot Soccer World Cup II
Using Ripple Down Rules for Actions and Planning
PRICAI '02 Proceedings of the 7th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Local Patching Produces Compact Knowledge Bases
EKAW '94 Proceedings of the 8th European Knowledge Acquisition Workshop on A Future for Knowledge Acquisition
Layered learning in multiagent systems
Layered learning in multiagent systems
Adaptive Ripple Down Rules method based on minimum description length principle
Intelligent Data Analysis
Improved knowledge acquisition for high-performance heuristic search
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Detection of CAN by ensemble classifiers based on ripple down rules
PKAW'12 Proceedings of the 12th Pacific Rim conference on Knowledge Management and Acquisition for Intelligent Systems
Ripple down rules for vietnamese named entity recognition
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part I
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This paper describes a system that allows soccer coaches to specify the behaviour of agents for the Robocup 2D soccer simulation domain [1]. The work we present is based on Generalised Ripple Down Rules [7,2] and allows the coach to interact directly with the system to incrementally model behaviours along with intermediate features during the knowledge acquisition process. The system was evaluated over a period of 6 months to measure the level of performance of the multi-agent teams created with the system and to gather feedback about the usability of the system. During this period the system was successfully used by four soccer coaches with differing levels of soccer and computer expertise. All coaches were able to use the system to develop teams that could play at a world class level against the finalists from the Robocup 2007 2D simulation tournament. The approach we present is general enough to be applied to any complex planning problem, with the requirement that a rich feature language is developed to support the specific domain.