Layered Multi-Template Retrieval Adaptation and Learning
Final technical rept. Jul 2000-Sep 2003
CARNEGIE-MELLON UNIV PITTSBURGH PA
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This effort was part of the DARPA Active Templates program 2000-2004 to revolutionize mission planning, mission execution, and related command and control processes. Extensive use is made of previous research in generative planning and learning, case-based and mixed-initiative plan adaptation, real-time integration of action and execution, and multi-agent control and learning. Technology was developed to support users in creating and managing template- based plan, allowing them to anticipate multiple contingencies and dynamically re-plan based on real-time sensory information. The research is grouped into the following themes 1 Allocation of communications spectrum frequencies, 2 Extraction of plan rationale and the learning of planning templates, 3 New abstraction techniques for reinforcement learning to improve the efficiency of automatic control algorithms, 4 Opponent modeling in dynamic multi-agent environment, 5 Multi-agent learning and limitations, and 6 Planning using symbolic model-based techniques. An extensive bibliography is included listing publications which describe the results of these research tasks in more detail.
- Command, Control and Communications Systems