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The Computational Theory of Intelligence: Applications to Genetic Programming and Turing Machines
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作者 daniel kovach 《International Journal of Modern Nonlinear Theory and Application》 2015年第1期10-20,共11页
In this paper, we continue the efforts of the Computational Theory of Intelligence (CTI) by extending concepts to include computational processes in terms of Genetic Algorithms (GA’s) and Turing Machines (TM’s). Act... In this paper, we continue the efforts of the Computational Theory of Intelligence (CTI) by extending concepts to include computational processes in terms of Genetic Algorithms (GA’s) and Turing Machines (TM’s). Active, Passive, and Hybrid Computational Intelligence processes are also introduced and discussed. We consider the ramifications of the assumptions of CTI with regard to the qualities of reproduction and virility. Applications to Biology, Computer Science and Cyber Security are also discussed. 展开更多
关键词 Artificial INTELLIGENCE COMPUTER SCIENCE INTELLIGENCE GENETIC PROGRAMMING GENETIC Algorithms Machine Learning
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The Computational Theory of Intelligence: Feedback
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作者 daniel kovach 《International Journal of Modern Nonlinear Theory and Application》 2017年第2期70-73,共4页
In this paper we discuss the applications of feedback to intelligent agents. We show that it adds a momentum component to the learning algorithm. We derive via Lyapunov stability theory the condition necessary in orde... In this paper we discuss the applications of feedback to intelligent agents. We show that it adds a momentum component to the learning algorithm. We derive via Lyapunov stability theory the condition necessary in order that the entropy minimization principal of computational intelligence is preserved in the presence of feedback. 展开更多
关键词 NEURAL NETWORKS FEEDBACK INTELLIGENCE COMPUTATION Artificial INTELLIGENCE LYAPUNOV Stability
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The Computational Theory of Intelligence: Information Entropy
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作者 daniel kovach 《International Journal of Modern Nonlinear Theory and Application》 2014年第4期182-190,共9页
This paper presents an information theoretic approach to the concept of intelligence in the computational sense. We introduce a probabilistic framework from which computation alintelligence is shown to be an entropy m... This paper presents an information theoretic approach to the concept of intelligence in the computational sense. We introduce a probabilistic framework from which computation alintelligence is shown to be an entropy minimizing process at the local level. Using this new scheme, we develop a simple data driven clustering example and discuss its applications. 展开更多
关键词 MACHINE LEARNING Artificial INTELLIGENCE ENTROPY COMPUTER SCIENCE INTELLIGENCE
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