Knowledge and Knowledge Acquisition in the Computational Context
Stephen B. Regoczei and Graeme Hirst
The enterprise of artificial intelligence (AI) has given rise to a new class of software systems. These software systems, commonly called expert systems, or knowledge-based systems, are distinguished in that they contain, and can apply, knowledge or some particular skill or expertise in the execution of a task. These systems embody, in some form, humanlike expertise. The construction of such software therefore requires that we somehow get hold of the knowledge and transfer it into the computer, representing it in a form usable by the machine. This total process has come to be called knowledge acquisition (KA). The necessity for knowledge representation (KR) -- the describing or writing down of the knowledge in machine-usable form -- underlies and shapes the whole KA process and the development of expert system software.
Concern with knowledge is nothing new, but some genuinely new issues have been introduced by the construction of expert systems. The processes of KA and KR are envisaged as the means through which software is endowed with expertise-producing knowledge. This vision, however, is problematic. The connection between knowledge and expertise itself is not clearly understood, though the phrases knowledge-based system and expert system tend to be used interchangeably, as if all expertise were knowledgelike. This haziness about basics also leads to the unrealistic expectation that the acquisition of knowledge in machine-usable form will convey powers of expert performance upon computer software. These assumptions are questionable. For a deeper understanding, we must clarify the concepts KA and KR, and the concept of knowledge itself, as they are used in the computer context. That is the first goal of this chapter. The second goal is to explicate the issues involved in KA and show how they are amenable to research by experimental or cognitive psychologists.
The chapter will be organized as follows. In the second section we will set the stage for cross-disciplinary discussion by sketching the history of AI and KA. In the third section, we try to answer the question, What is knowledge? by examining the various approaches that people have taken in trying to grasp the nature of knowledge. In the fourth section, we discuss the KA problem. In particular, we present a model of the KA process to reconcile and pull together the various approaches to KA that are found in the literature. This basic model of KA will be used in the commentaries chapter (chapter 17) to compare the contributions to this volume. In the present introductory chapter, we outline some crucial current issues, especially those that could be fruitfully addressed by experimental psychologists, and as a conclusion we try to point to some future directions for research.