Modeling of the process of discussion in the

I. A. Pshenokova

News of the Kabardin-Balkar scientific center of RAS, no. 6-3 (2018), pp. 83-88.

Résumé

The paper presents a simulation of the process of reasoning in a multi-agent cognitive system for the formation of an action plan for self-learning and self-organization in order to synthesize an intelligent solution of the problem. The developed model allows to present the process of reasoning in the form of cognitive blocks, which, due to self-organization and self-learning of the multi-agent system inside them, synthesize intelligent solutions that allow the user to effectively find answers to various complex problems and everyday tasks.

Détail

Keywords: artificial intelligence systems, multi-agent systems, cognitive architectures, decisionmaking systems, self-learning systems.

Bibliographie

[1] Minsky Marvin, The Society of Mind, Simon, Schuster, New York, 1986

[2] Newell, Simon, Human Problem Solving, Prentice-Hall, Englewood Cliffs, N.J., 1972 | MR

[3] S. Rassel, P. Norvig, Iskusstvennyi intellekt. Sovremennyi podkhod, 2-e izd., Vilyams, M., 2007, 1410 pp.

[4] Smed and Hakonen, Algorithms and Networking for Computer Games, John Wiley Sons., 2006

[5] Pamela McCorduck, Machines Who Think, 2nd edition, A. K. Peters, Ltd, Natick, MA, 2004

[6] Monty Newborn, Deep Blue: An Artificial Intelligence Milestone, Springer, 2003, 346 pp. | MR | Zbl

[7] The AI Business: The commercial uses of artificial intelligence, eds. Patrick Winston and Karen A. Prendergast.

[8] Robert McNair, “Build Your Own Expert System by Chris Naylor”, The Statistician, 34:2 (1985), 255 | DOI | Zbl

[9] W3C, Speech Input API Specification [Elektronnyi resurs] https:// www.w3.org/2005/Incubator/htmlspeech/2010/10/google-api-draft.html

[10] Kai Fu Li, Hsiao-Wuen Hon, “An overview of the Sphinx Speech Recognition Systems [Elektronnyi resurs]”, The Robotics Institute – Carnegie Mellon University http://www.ri.cmu.edu/pub_files

[11] J. C. Blanchette, M. Fleury, P. Lammich, C. Weidenbach, “A Verified SAT Solver Framework with Learn, Forget, Restart, and Incrementality”, Journal of Automated Reasoning, 61(1-4) (2018) | DOI | MR

[12] Robert Yang, Igor Ganichev, Xiao-Jing Wang, Jonathon Shlens and David Sussillo, “A Dataset and Architecture for Visual Reasoning with a Working Memory”, 2018, arXiv: https://arxiv.org/pdf/1803.06092.pdf

[13] David Ha, Douglas Eck, “A Neural Representation of Sketch Drawings”, arXiv: (16 Apr. 2017) 1704.03477v2[cs.NE]

[14] Ray Kurzweil, The Singularity is Near, Penguin Group, 2005, 135–136 pp.

[15] D. A. Pospelov, Modelirovanie rassuzhdenii. Opyt analiza myslitelnykh aktov, Radio i svyaz, M., 1989

[16] V. N. Vagin, A. P. Eremeev, “Issledovaniya i razrabotki kafedry prikladnoi matematiki po konstruirovaniyu intellektualnykh sistem podderzhki prinyatiya reshenii na osnove netraditsionnykh logik”, Vestnik MEI, 2008, no. 5, 16–26

[17] P. R. Varshavskii, A. P. Eremeev, “Realizatsiya metodov poiska resheniya na osnove analogii i pretsedentov v sistemakh podderzhki prinyatiya reshenii”, Vestnik MEI, 2006, no. 2, 77–87

[18] K. J. Hammond, “CHEF: A Model of Case-Based Planning”, Proc. American Association for Artificial Intelligence, AAAI-86, August 1986, Philadelphia, PA, US

[19] C. Antoniou, Non-monotonic reasoning, The MIT Press, 1997 | MR

[20] V. I. Gorodetskii, A. L. Tulupev, “Formirovanie neprotivorechivykh baz znanii s neopredelennostyu”, Izv. RAN, Ser. Teoriya i sistemy upravleniya, 5, 1997, 33–42 | MR | Zbl

[21] Richard Evans, Edward Grefenstette, “Learning Explanatory Rules from Noisy Data”, Journal of Artificial Intelligence Research, 61 (2018), 1–64 | DOI | MR | Zbl

[22] Z. V. Nagoev, Intellektika ili Myshlenie v zhivykh i iskusstvennykh sistemakh, Izdatelstvo KBNTs RAN, Nalchik, 2013

[23] I. A. Pshenokova, “Imitatsionnoe modelirovanie sistem obvolakivayuschego intellekta na osnove samoorganizuyuscheisya multiagentnoi rekursivnoi kognitivnoi arkhitektury”, Izvestiya KBNTs RAN, 2018, no. 3 (83), 21–27