Second generation expert systems

Acquiring knowledge from a human expert is a major problem when building a knowledge-based system. Aquinas, an expanded version of the Expertise Transfer System (ETS), is a knowledge-acquisition workbench that combines ideas from psychology and knowledge-based systems research to support knowledge-acquisition tasks. These tasks include eliciting distinctions, decomposing problems, combining uncertain information, incremental testing, integration of data types, automatic expansion and refinement of the knowledge base, use of multiple sources of knowledge and providing process guidance. Aquinas interviews experts and helps them analyse, test, and refine the knowledge base. Expertise from multiple experts or other knowledge sources can be represented and used separately or combined. Results from user consultations are derived from information propagated through hierarchies. Aquinas delivers knowledge by creating knowledge bases for several different expert-system shells. Help is given to the expert by a dialog manager that embodies knowledge-acquisition heuristics. Aquinas contains many techniques and tools for knowledge acquisition; the techniques combine to make it a powerful testbed for rapidly prototyping portions of many kinds of complex knowledge-based systems.

Expertise transfer and complex problems: using AQUINAS as a knowledge-acquisition workbench for knowledge-based systems

Constraint reasoning based on interval arithmetic: the tolerance propagation approach

Publisher Summary This chapter explains the formation and use of abstract concepts in design. The principal focus of this chapter is on acquiring expert design knowledge. One promising solution uses inductive methods for supervised and unsupervised concept learning. The chapter discusses the relationships between these learning strategies and design. Design is a complex task. The chapter focuses on the synthesis aspect of design, namely, the assembly of candidate designs from an infinite set of alternatives. The synthesis is an ill-structured process with respect to all three characteristics of such problems. The only process that can reduce the complexity of synthesis is the acquisition and reorganization of knowledge. The chapter presents BRIDGER, which is a concept formation system that partially automates the acquisition of design knowledge for a class of design domains in which the structure of the artifact is fixed. Ideally, learning should render synthesis more efficient by allowing a design system to construct designs appropriate to the specification predicates.

The formation and use of abstract concepts in design

As expert system technology spreads, the need for verification of system knowledge assumes greater importance. This paper addresses the issues involved in demonstrating a rule-based system to be free from error. A holistic perspective is adopted, wherein sorces, manifestations, and effects of errors are identified. A general taxonomy is created, and the implications for system performance and development outlined. Existing strategies for knowledge verification are surveyed, their applicability assessed, and some directions for systematic verification suggested.

Issues in the verification of knowledge in rule-based systems

Knowledge-Based Problem Solving

Finding method in Madness.

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Knowledge-Based Problem Solving

Finding method in Madness.