This paper gives an overview of the development of the field of Knowledge Engineering over the last 15 years. We discuss the paradigm shift from a transfer view to a modeling view and describe two approaches which considerably shaped research in Knowledge Engineering: Role-limiting Methods and Generic Tasks. To illustrate various concepts and methods which evolved in recent years we describe three modeling frameworks: CommonKADS, MIKE and PROTEGE-II. This description is supplemented by discussing some important methodological developments in more detail: specification languages for knowledge-based systems, problem-solving methods and ontologies. We conclude by outlining the relationship of Knowledge Engineering to Software Engineering, Information Integration and Knowledge Management.

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Knowledge engineering: principles and methods

http://www.cs.ucf.edu/%7Elboloni/Teaching/EEL6938_2007/papers/Clancey-Situated-Action-Neuro-Interpretation.pdf

Situated Action: A Neuropsychological Interpretation Response to Vera and Simon

We address the problem of highly varied and inconsistent usage of terms by the knowledge technology community in the area of knowledge-level modelling. It is arguably difficult or impossible for any standard set of terms and definitions to be agreed on. However, de facto standard usage is already emerging within and across certain segments of the community. This is very difficult to see, however, especially for newcomers to the field. It is the goal of this paper to identify and reflect the most common usage of terms as currently found in the literature. To this end, we introduce and define the concept of a knowledge level model, comparing how the term is used today with Newell's original usage. We distinguish two major types of knowledge level model: ontologies and problem solving models. We describe what an ontology is, what they may be used for and how they are represented. We distinguish various kinds of ontologies and define a number of additional related concepts. We describe what is meant by a problem solving model, what they are used for, and attempt to clarify some terminological confusion that exists in the literature. We define what is meant by the term ‘problem’, and some common notions used to characterise and represent problems. We introduce and describe the ideas of tasks, problem solving methods and a variety of other important related concepts.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0269888998001040

Knowledge level modelling: concepts and terminology

Knowledge acquisition research supports the generation of knowledge-based systems through the development of principles, techniques, methodologies and tools. What differentiates knowledge-based system development from conventional system development is the emphasis on in-depth understanding and formalization of the relations between the conceptual structures underlying expert performance and the computational structures capable of emulating that performance. Personal construct psychology is a theory of individual and group psychological and social processes that has been used extensively in knowledge acquisition research to model the cognitive processes of human experts. The psychology takes a constructivist position appropriate to the modelling of human knowledge processes, but develops this through the characterization of human conceptual structures in axiomatic terms that translate directly to computational form. In particular, there is a close correspondence between the intensional logics of knowledge, belief and action developed in personal construct psychology, and the intensional logics for formal knowledge representation developed in artificial intelligence research as term subsumption, or KL-ONE-like, systems. This paper gives an overview of personal construct psychology and its expression as an intensional logic describing the cognitive processes of anticipatory agents, and uses this to survey knowledge acquisition tools deriving from personal construct psychology.

Knowledge acquisition tools based on personal construct psychology

Diagnostic Reasoning and Medical Expertise

Machine learning will never progress beyond its current state until people realize that knowledge is not a substance that can be stored. Knowledge acquisition, in particular, is a process of developing computer models, often for the first time, not a process of transferring or accessing statements or diagrams that are already written down and filed away in an expert’s mind. The “knowledge acquisition bottleneck” is a wrong and misleading metaphor, suggesting that the problem is to squeeze a large amount of already-formed concepts and relations through a narrow communication channel; the metaphor seriously misconstrues the theory formation process of computer modeling. The difficulties of choosing and evaluating knowledge acquisition methods are founded on a number of related misconceptions, clarified as follows: 1) the primary concern of knowledge engineering is modeling systems in the world (not replicating how people think—a matter for psychology); 2) knowledge-level analysis is how observers describe and explain the recurrent behaviors of a situated system, that is, some system interacting with an embedding environment; the knowledge level describes the product of an evolving, adaptive interaction between the situated system and its environment, not the internal, physical processes of an isolated system; 3) modeling intelligent behavior is fraught with frame-of-reference confusions, requiring that we tease apart the roles and points of view of the human expert, the mechanical devices he interacts with, the social and physical environment, and the observer-theoretician (with his own interacting suite of recording devices, representations, and purposes). The challenge to knowledge acquisition today is to clarify what we are doing (computer modeling), clarify the difficult problems (the nature of knowledge and representations), and reformulate our research program accordingly.

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William J. Clancey

Papers

The Knowledge Level Reinterpreted: Modeling How Systems Interact