Knowledge and knowledge acquisition in the computational context. Regoczei, S. & Hirst, G. The psychology of expertise: Cognitive research and empirical AI, pages 12–25. Springer-Verlag, New York, 1992.
abstract   bibtex   

The enterprise of artificial intelligence 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—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 knowledge-like. 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 of knowledge acquisition and knowledge representation, 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 artificial intelligence 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 of this book) 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.

@InBook{	  regoczei1,
  author	= {Stephen Regoczei and Graeme Hirst},
  chapter	= {Knowledge and knowledge acquisition in the computational
		  context},
  editor	= {Robert R. Hoffmann},
  title		= {The psychology of expertise: Cognitive research and
		  empirical AI},
  address	= {New York},
  publisher	= {Springer-Verlag},
  year		= {1992},
  pages		= {12--25},
  abstract	= {<P> The enterprise of artificial intelligence 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 <I>knowledge acquisition</I> (KA). The
		  necessity for knowledge representation---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.</p> <P> 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 knowledge-like.
		  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 of knowledge acquisition and knowledge
		  representation, 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.</p>
		  <P> 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 artificial
		  intelligence 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 of this book) 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.</p>}
}
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