The knowledge instinct, cognitive algorithms, modeling of language and cultural evolution

Leonid I. Perlovsky

doi:10.1117/12.786436

1 May 2008 The knowledge instinct, cognitive algorithms, modeling of language and cultural evolution

Leonid I. Perlovsky

Proceedings Volume 6964, Evolutionary and Bio-Inspired Computation: Theory and Applications II; 696402 (2008) https://doi.org/10.1117/12.786436
Event: SPIE Defense and Security Symposium, 2008, Orlando, Florida, United States

Abstract

The talk discusses mechanisms of the mind and their engineering applications. The past attempts at designing "intelligent systems" encountered mathematical difficulties related to algorithmic complexity. The culprit turned out to be logic, which in one way or another was used not only in logic rule systems, but also in statistical, neural, and fuzzy systems. Algorithmic complexity is related to Godel's theory, a most fundamental mathematical result. These difficulties were overcome by replacing logic with a dynamic process "from vague to crisp," dynamic logic. It leads to algorithms overcoming combinatorial complexity, and resulting in orders of magnitude improvement in classical problems of detection, tracking, fusion, and prediction in noise. I present engineering applications to pattern recognition, detection, tracking, fusion, financial predictions, and Internet search engines. Mathematical and engineering efficiency of dynamic logic can also be understood as cognitive algorithm, which describes fundamental property of the mind, the knowledge instinct responsible for all our higher cognitive functions: concepts, perception, cognition, instincts, imaginations, intuitions, emotions, including emotions of the beautiful. I present our latest results in modeling evolution of languages and cultures, their interactions in these processes, and role of music in cultural evolution. Experimental data is presented that support the theory. Future directions are outlined.

Citation Download Citation

Leonid I. Perlovsky "The knowledge instinct, cognitive algorithms, modeling of language and cultural evolution", Proc. SPIE 6964, Evolutionary and Bio-Inspired Computation: Theory and Applications II, 696402 (1 May 2008); https://doi.org/10.1117/12.786436

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KEYWORDS

Cognitive modeling

Logic

Cognition

Data modeling

Mathematical modeling

Consciousness

Neurons

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