Algorithms of artificial intuition for implementation of strong ai

Yu. A. Prokopchuk

Abstract


Purpose. Intuition and Logic are two strategies for prediction and problem solving. Most humans have not been taught logical thinking, but most humans are still intelligent. Contrary to the majority view, it is implausible that the brain should be based on Logic. How does Intuition work? I believe intelligence emerges from millions of nested micro-intuitions, and that Artificial Intelligence requires Artificial Intuition. It is necessary to introduce Human-like Intuition Mechanism into Artificial Intelligence. The aim of this work is the development of constructive algorithms for artificial intuition. Methodology. Modeling the work of intuition is proposed on the basis of the Limiting-Generalizations Paradigm (LGP). Findings. The key components of the intuition model are: basic entities of the LGP; a task-inductor space, event space, an "artificial connectom", coherence mechanisms; Thin Slices. Originality. With the help of formal models and constructive algorithms, it is shown that the basis for rapid cognition and intuition is the adaptive unconscious - the thought process that works automatically when we have relatively little information to make a decision. These models form a new approach to the concept of "Strong AI". Practical value. The proposed model is the methodological basis for creating promising IT, as well as intuitive agents, robots.

Keywords


strong AI, artificial intuition; cognitive approach; intuitive and creative thinking; natural logic; limiting-generalizations paradigm

References


Prokopchuk Yu. A. Nabrosok formalnoy teorii tvorchestva [Sketch of the Formal Theory of Creativity]. Monograph. Dnipro: PSACEA Publ., 2017, 452 p. (in Russsian).

Prokopchuk Yu. A. Printsip predelnykh obobshcheniy: metodologiya, zadachi, prilogeniya [Principle of Limiting Generalizations: Methodology, Problems, and Applications]. Monograph. Dnepropetrovsk: Institute of Technical Mechanics of the NAS of Ukraine Publ., 2012, 384 p. (in Russsian)

Gladwell M. Blink: The Power of Thinking Without Thinking. Back Bay Books, 2007, 320 p.

Kahneman D. Thinking, Fast and Slow. Pub Farrar, Straus and Giroux, 2011, 499 p.

Klein G. A naturalistic decision making perspective on studying intuitive decision making . Journal of Applied Research in Memory and Cognition 4, 2015, pp. 164–168.

Maldonato M. and Dell'Orco S. Natural Logic: Exploring Decision and Intuition. – UK: Sussex Academic Press, 2011, 112 p.

Todd P.and Gigerenzer G. Putting naturalistic decision making into the adaptive toolbox . Journal of Behavioral Decision Making. 14 (5), 2001. – pp. 381–383. doi:10.1002/bdm.396.


GOST Style Citations


1. Прокопчук Ю. А. Набросок формальной теории творчества / Ю. А. Прокопчук. – Днепр : ПГАСА, 2017. – 452 с.


2. Прокопчук Ю. А. Принцип предельных обобщений: методология, задачи, приложения / Ю. А. Прокопчук. – Днепропетровск: Ин-т технической механики НАНУ и НКАУ, 2012.– 384 с.


3. Gladwell M. Blink: The Power of Thinking Without Thinking / M. Gladwell. — Back Bay Books, 2007. — 320 p.


4. Kahneman D. Thinking, Fast and Slow / D. Kahneman. – Pub Farrar, Straus and Giroux, 2011. – 499 p.


5. Klein G. A naturalistic decision making perspective on studying intuitive decision making / G. Klein // Journal of Applied Research in Memory and Cognition 4, 2015. - P. 164–168.


6. Maldonato M. Natural Logic: Exploring Decision and Intuition / M. Maldonato, S. Dell'Orco. – UK: Sussex Academic Press, 2011. - 112 p.


7. Todd P., Gigerenzer G. Putting naturalistic decision making into the adaptive toolbox / P. Todd, G. Gigerenzer // Journal of Behavioral Decision Making. 14 (5), 2001. – P. 381–383. doi:10.1002/bdm.396.



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