Evolutionary Method of Constructing Artificial Intelligence Systems

An evolutionary model of constructing artificial intelligence is presented, which is destined for designing and developing intelligent systems. The model allows describing a variety of subject areas with constructing knowledge bases. It has universal means to formally describe tasks and environments...

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Vydané v:Cybernetics and systems analysis Ročník 55; číslo 1; s. 1 - 9
Hlavní autori: Anisimov, A. V., Marchenko, O. O., Zemlianskyi, V. R.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Springer US 01.01.2019
Springer
Springer Nature B.V
Predmet:
Agents (artificial intelligence)
Algorithms
Analysis
Artificial Intelligence
Control
Cybernetics
Evolution (Biology)
Evolutionary algorithms
Genetic algorithms
Intelligent agents
Knowledge bases (artificial intelligence)
Mathematics
Mathematics and Statistics
Methods
Organizational communication
Processor Architectures
Software Engineering/Programming and Operating Systems
Systems Theory
evolutionary programming
artificial intelligence
multiagent system
ISSN:1060-0396, 1573-8337
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Popis
Shrnutí:An evolutionary model of constructing artificial intelligence is presented, which is destined for designing and developing intelligent systems. The model allows describing a variety of subject areas with constructing knowledge bases. It has universal means to formally describe tasks and environments for implementing computational processes to solve them. The key basic element of the proposed model is the so-called ALF, i.e., an intelligent agent with the abilities to self-learning, communication, self-organization, and joint actions with similar agents. The development of ALF agents is based on evolutionary principles implemented using genetic algorithms. The proposed approach is implemented in the form of a game model. The developed structure and functionality of ALF agents stipulate the flexibility and efficiency of the model, which is confirmed by experiments.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1060-0396
1573-8337
DOI:10.1007/s10559-019-00106-x