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A framework for skill acquisition is proposed that includes two major stages in the development of a cognitive skill: a declarative stage in which facts about the skill domain are interpreted and a procedural stage in which the domain knowledge is directly embodied in procedures for performing the skill. This general framework has been instantiated in the(More)
This study examines the problem of belief revision, defined as deciding which of several initially accepted sentences to disbelieve, when new information presents a logical inconsistency with the initial set. In the first three experiments, the initial sentence set included a conditional sentence, a non-conditional (ground) sentence, and an inferred(More)
Simple belief-revision tasks were defined by a giving subjects a conditional premise, (p—>q), a categorical premise, (p, for a modus-ponens belief-set, or ~q, for a modus tollens belief-set), and the associated inference (q or ~p, respectively). "New" information contradicted the initial inference (~ q or p, respectively). Subjects indicated their degree of(More)
Continued practice on a task is chorocterized by several quantitative and qualitative changes in performance. The most salient is the speed-up in the time to execute the tosk. To account for these effects, some models of skilled performance have proposed automatic mechanisms that merge knowlege structures associated with the task into fewer, larger(More)
This article describes LAIR, a constructive induction system that acquires conjunctive concepts by applying a domain theory to introduce new features into the evolving concept description. Each acquired concept is added to the domain theory, making LAIR a closed-loop learning system that weakens the inductive bias with each iteration of the learning loop.(More)
This article uses simulation as an empirical method for identifying process models of strategy effects in a category-learning task. A general set of learning assumptions defined a symbolic learning framework in which alternative simulation models were defined and tested. The goal was to identify process models that could account for previously reported data(More)
ion of Implicit Domain Concepts The fact that EUREKA indexes the enhanced problem representation means that its expert-level schemas are not limited to the problem’s initial descriptors. Instead, physics concepts and other inferences about object relations and features are eligible to become norms and indices. This is important for abstracting useful(More)
This is a position paper concerning the role of empirical studies of human default reasoning in the formalization of AI theories of default reasoning. We note that AI motivates its theoretical enterprise by reference to human skill at default reasoning, but that the actual research does not make any use of this sort of information and instead relies on(More)
In this paper, we describe the Adaptive Place Advisor, a conversational interface designed to help users decide on a destination. We view the selection of destinations as an interactive process of constraint satisfaction, with the advisory system proposing attributes and the human responding. We further characterize this task in terms of heuristic search,(More)