An explanation-based approach to assigning credit
An explanation-based approach to assigning credit
Failure-driven learning as model-based self-redesign
Failure-driven learning as model-based self-redesign
Multistrategy Adaptive Path Planning
IEEE Expert: Intelligent Systems and Their Applications
Task-Structures, Knowledge Acquisition and Learning
Machine Learning
Integration of case-based reasoning and model-based reasoning for adaptive design problem-solving
Integration of case-based reasoning and model-based reasoning for adaptive design problem-solving
Some studies in machine learning using the game of checkers
IBM Journal of Research and Development
Meta-case-Based Reasoning: Using Functional Models to Adapt Case-Based Agents
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Task-Structure Based Mediation: The Travel-Planning Assistant Example
AI '00 Proceedings of the 13th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
Learning about Constraints by Reflection
AI '01 Proceedings of the 14th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
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Blame assignment is a classical problem in learning and adaptation. Given a problem solver that fails to deliver the behaviors desired of it, the blame-assignment task has the goal of identifying the cause(s) of the failure. Broadly categorized, these causes can be knowledge faults (errors in the organization, content, and representation of the problem-solver's domain knowledge) or processing faults (errors in the content, and control of the problem-solving process). Much of AI research on blame assignment has focused on identifying knowledge and control-of-processing faults based on the trace of the failed problem-solving episode. In this paper, we describe a blame-assignment method for identifying content-of-processing faults, i.e., faults in the specification of the problem-solving operators. This method uses a structure-behavior-function (SBF) model of the problem-solving process, which captures the functional semantics of the overall task and the operators of the problem solver, the compositional semantics of its problem-solving methods that combine the operators' inferences into the outputs of the overall task, and the "causal" inter-dependencies between its tasks, methods and domain knowledge. We illustrate this model-based blame-assignment method with examples from AUTOGNOSTIC.