Domain-independent planning: representation and plan generation
Artificial Intelligence
Diagnostic reasoning based on structure and behavior
Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Artificial Intelligence
Dynamic Memory: A Theory of Reminding and Learning in Computers and People
Dynamic Memory: A Theory of Reminding and Learning in Computers and People
A Computer Model of Skill Acquisition
A Computer Model of Skill Acquisition
Explanation-Based Generalization: A Unifying View
Machine Learning
Explanation-Based Learning: An Alternative View
Machine Learning
AMORD explicit control of reasoning
Proceedings of the 1977 symposium on Artificial intelligence and programming languages
Adaptive execution in complex dynamic worlds
Adaptive execution in complex dynamic worlds
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The goal of our research is to construct an integrated model of planning and learning that can account for the acquisition of new planning knowledge. Our approach involves the use of model-based reasoning. In this approach, the system monitors its performance by comparing it with expectations derived from a model of the system's planning architecture. The arguments relating the system's expectations to its underlying model of the planning process are encoded in the form of explicit justification structures. When the system's actual performance diverges from its expectations, it traces back through these justification structures, looking to fault the setting of some controllable parameter of the planner. When such a controllable parameter is isolated, a repair is then effected, in the form of an adjustment to one of these parameters.