classic is a recently-developed knowledge representation system that follows the paradigm originally set out in the kl-one system: it concentrates on the definition of structured concepts, their organization into taxonomies, the creation and manipulation of individual instances of such concepts, and the key inferences of subsumption and classification. Rather than simply presenting a description of classic, we complement a brief system overview with a discussion of how to live within the confines of a limited object-oriented deductive system. By analyzing the representational strengths and weaknesses of classic, we consider the circumstances under which it is most appropriate to use (or not use) it. We elaborate a knowledge-engineering methodology for building kl-one-style knowledge bases, with emphasis on the modeling choices that arise in the process of describing a domain. We also address some of the key difficult issues encountered by new users, including primitive vs. d...

This article presents a computational model of the learning of diagnostic knowledge based on observations of human operators engaged in a real-world troubleshooting task. We present a model of problem solving and learning in which the reasoner introspects about its own performance on the problem solving task, identifies what it needs to learn to improve its performance, formulates learning goals to acquire the required knowledge, and pursues its learning goals using multiple learning strategies. The model is implemented in a computer system which provides a case study based on observations of troubleshooting operators and protocol analysis of the data gathered in the test area of an operational electronics manufacturing plant. The model is intended as a computational model of human learning; in addition, it is computationally justified as a uniform, extensible framework for multistrategy learning. Technical Report GIT-CC-93/67, College of Computing, Georgia Institute of Technology, A...

ion Axioms ? Luca Console 1 and Daniele Theseider Dupr'e 2 1 Dipartimento di Matematica e Informatica, Universit`a di Udine Via Zanon 6, 33100 Udine, Italy 2 Dipartimento di Informatica, Universit`a di Torino, Corso Svizzera 185, 10149 Torino, Italy Abstract. This paper deals with abductive reasoning on knowledge bases that are expressed at different levels of abstraction, but are not necessarily organized as a set of increasingly more abstract models, each one giving a complete (even if abstracted) description of a domain. We claim that the search for abductive explanations in such a context and, in particular, the choice of the "right" level at which explanations have to be determined, should be driven by the available observations in such a way that explanations involving low-level phenomena are allowed only if there are specific observations related to them, or higher-level explanations cannot be found. We present formal definitions following this principle and we discuss ho...

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