Layered Multi-Template Retrieval Adaptation and Learning
Manuela Veloso · 2004
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.