Abstract
Basic methodological assumptions of methods which belong to symbolic Artificial Intelligence are presented in this chapter.
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Notes
- 1.
For example, knowledge is defined in the form of graphs, logic formulas, symbolic rules, etc. Methods of symbolic AI are developed on the basis of logic, theory of formal languages, various areas of discrete mathematics, etc.
- 2.
For example, operations in the form of inference rules in logic, productions in the theory of formal languages, etc.
- 3.
A heuristic algorithm is an algorithm which can generate an accepted solution of a problem although we cannot formally prove the adequacy of the algorithm w.r.t. the problem. The term heuristics was introduced by the outstanding mathematician George Pólya. Newell attended lectures delivered by Pólya at Stanford University.
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The Object-Oriented paradigm is, nowadays, the second standard approach.
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In Latin imperativus means commanded.
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We declare required properties of a solution of a problem.
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This paradigm is also used nowadays beyond AI. For example, such programming languages as SQL and HTML are also based on the declarative paradigm.
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A more detailed description of the functional approach is included in Sect. 6.5.
- 9.
In such a situation we say that the system has matched a certain fact (facts) stored in the working memory to the rule.
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Gilbert Ryle—a professor of philosophy at Oxford University, one of the most eminent representatives of analytic philosophy, the editor of the prestigious journal Mind.
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The Chomsky theory is presented in the next section.
- 12.
A normalization of a semantic representation is necessary if it is to be performed automatically, because all sentences which have the same meaning, e.g., John has lent a book to Mary., Mary has borrowed a book from John. should have the same representation.
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Flasiński, M. (2016). Symbolic Artificial Intelligence. In: Introduction to Artificial Intelligence. Springer, Cham. https://doi.org/10.1007/978-3-319-40022-8_2
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DOI: https://doi.org/10.1007/978-3-319-40022-8_2
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