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REPRESENTATION OF KNOWLEDGE AND ALGORITHMS FOR ECONOMIC SYSTEMS

Emilia Vasile () and Danut-Octavian Simion ()
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Emilia Vasile: Athenaeum University, Bucharest, Romania
Danut-Octavian Simion: Athenaeum University, Bucharest, Romania

Internal Auditing and Risk Management, 2017, vol. 48, issue 4, 7-20

Abstract: The paper presents the representation of knowledge and algorithms for economic systems that ensure the right way to solve various problems. An economic system is a complex software program that must answer to the requirements of the inside environment of an economical society and the outside environment which provides inputs for these types of systems. A complex problem requires a different approach such as an algorithm derived from artificial intelligence that can process representations of knowledge. The new techniques of the AI have solved complex tasks, which in the past could not be solved or were much more difficult - costly. Intelligent activity is determined by the human interaction with the external environment, where feedback occurs, closed through the sensory system. In the inner world, reasoning, which determines action decisions, is based on external data, obtained through perception, but also on the basis of an “internal†model of the world. A economical system is considered to have the intelligence property, whether if it can adapt itself to new situations, has the ability to reason, that is, to understand the links between facts, to discover meanings and to recognize the truth. Also, an intelligent system can learn, in other words, improve its performance based on experience. At this point, such economical systems implements high level algorithms such as backtracking methods that applies to problems where the solutions can be represented as a vector containing different variables.

Keywords: Representation of knowledge; business environment; programing algorithms; information systems; artificial intelligence (search for similar items in EconPapers)
JEL-codes: C23 C26 C38 C55 C81 C87 (search for similar items in EconPapers)
Date: 2017
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