A Computer Model of Skill Acquisition
A Computer Model of Skill Acquisition
Explanation-Based Generalization: A Unifying View
Machine Learning
Representing and Reasoning About Change in Geologic Interpretation
Representing and Reasoning About Change in Geologic Interpretation
Planning for Conjunctive Goals
Planning for Conjunctive Goals
A structure for plans and behavior.
A structure for plans and behavior.
Classifying and recovering from sensing failures in autonomous mobile robots
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Combining specialized reasoners and general purpose planners: a case study
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 1
Using quantitative and qualitative constraints in models of cardiac electrophysiology
Artificial Intelligence in Medicine
Artificial Intelligence in Medicine
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We present a problem solving paradigm called generate, test and debug (GTD) that combines associational rules and causal models, producing a system with both the efficiency of rules and the breadth of problem solving power of causal models. The generator uses associational rules to generate plausible hypotheses; the tester uses causal models to test the hypotheses and produce a detailed characterization of the discrepancy in case of failure. The debugger uses the ability to reason about the causal models, along with a body of domain-independent debugging knowledge, to determine how to repair the buggy hypotheses. The GTD paradigm has been implemented and tested in three different domains; we report in detail on its application to our principal domain of geologic interpretation. We also explore in some depth the character of the problems for which GTD is well suited and consider the character of the knowledge required for successful use of the paradigm.