A formal theory of plan recognition
A formal theory of plan recognition
Programming by demonstration: an inductive learning formulation
IUI '99 Proceedings of the 4th international conference on Intelligent user interfaces
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AGENTS '00 Proceedings of the fourth international conference on Autonomous agents
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PRIMA '98 Selected papers from the First Pacific Rim International Workshop on Multi-Agents, Multiagent Platforms
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PRIMA '99 Proceedings of the Second Pacific Rim International Workshop on Multi-Agents: Approaches to Intelligent Agents
Programming by Demonstration Using Version Space Algebra
Machine Learning
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The Knowledge Engineering Review
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Plan recognition for interface agents
Artificial Intelligence Review
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AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Goal recognition through goal graph analysis
Journal of Artificial Intelligence Research
Adaptive web sites: an AI challenge
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
Activity recognition: linking low-level sensors to high-level intelligence
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Domain Independent Goal Recognition
Proceedings of the 2010 conference on STAIRS 2010: Proceedings of the Fifth Starting AI Researchers' Symposium
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The bulk of previous work on goal and plan recognition may be crudely stereotyped in one of two ways. "Neat" theories -- rigorous, justified, but not yet practical. "Scruffy" systems -- heuristic, domain specific, but practical. In contrast, we describe a goal recognition module that is provably sound and polynomial-time and that performs well in a real domain. Our goal recognizer observes actions executed by a human, and repeatedly prunes inconsistent actions and goals from a graph representation of the domain. We report on experiments on human subjects in the Unix domain that demonstrate our algorithm to be fast in practice. The average time to process an observed action with an initial set of 249 goal schemas and 22 action schemas was 1.4 cpu seconds on a SPARC-10.