A Model-Based Approach to Blame Assignment: Revising the Reasoning Steps of Problem Solvers

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 problemsolver'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-assignmentmethod 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 problemsolving 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-assignmentmethod with examples from AUTOGNOSTIC.