Logic-Based Reasoning in the Framework of Artificial Intelligence

Logic-Based Reasoning in the Framework of Artificial Intelligence

Xenia Naidenova (Military Medical Academy, Russia)
DOI: 10.4018/978-1-60566-810-9.ch002
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Abstract

This chapter focuses on the tasks of knowledge engineering related mainly to knowledge acquisition and modeling integrated logic-based inference. We have overlooked the principal and more important directions of researches that pave the ways to understanding and modeling human plausible (commonsense) reasoning in computers.
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The Tasks Of Knowledge Engineering

Knowledge engineering is the most ancient human activity. Knowledge is necessary for solving even the simplest tasks and for the very life of man. Knowledge is extracted practically from everywhere and by a lot of different methods. Transferring and accumulating knowledge are not possible without the means of natural languages. Furthermore, knowledge is specialized, i.e. it is task-dependent. Inasmuch as the quantity of tasks solved by man is multiplied and knowledge is multiplied. Knowledge engineering covers extracting, collecting, analyzing, modeling, representing, validating, and using knowledge, in general, acquiring and managing knowledge. In our knowledge-driven society, the knowledge engineering systems have their place as an important mainstream technology. Figure 1 illustrates the main components of knowledge engineering.

Figure 1.

Constitutive elements of knowledge engineering

An important part of knowledge engineering is the knowledge acquisition the task of which is to extract knowledge from data into the forms that can be used by computers. Knowledge is obtained directly from domain’s specialists (experts) or from the other sources, in which knowledge is potentially contained (texts in natural languages, data banks, images, the Internet and so on).

The stages of knowledge acquisition are actually the stages of intellectual activity on processing the data. The purpose of this work is to understand, what useful information exists in or can be extracted from the data, such as facts, events, situations, and links between them. This process is tightly connected with learning. Figure 2 illustrates the idea that learning is a binding link in knowledge acquisition.

Figure 2.

Learning as a binding unit in knowledge acquisition

We note the following characteristics of knowledge acquisition:

  • 1.

    multi-level

  • 2.

    multi-systemic

  • 3.

    interactive

  • 4.

    learning-based

  • 5.

    purpose-dependent

  • 6.

    task dependent

  • 7.

    continuous

  • 8.

    cyclic (back forward links).

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