ISSN 2071-8594

Russian academy of sciences

Editor-in-Chief

Gennady Osipov

Gennady S. Osipov, Aleksandr I. Panov Relationships and operations in agent's sign-based model of the world

Abstract.

According to modern theories of mental function's emergence and the role of neurophysiological processes therein, the mental function formation is associated with the existence or communicative synthesis of specific information structures containing three information types of different origin: information coming from the external environment, information extracted from the memory and information coming from motivation centres. The binding of these components into a single entity is ensured by naming them; this also provides for the emerging structures’ stability. We call such information structures as signs due to their resemblance to similar structures that have been studied in semiotics. The set of signs formed by the actor during activities and communication forms his sign based world model reflecting his ideas about the environment, himself and other actors. The sign based world model enables the setting and resolution of a number of tasks arising for intelligent agents and their coalitions during behaviour modeling , such as goal-setting, purposeful behaviour synthesis, role distribution, and the interaction of agents in the coalition. The paper considers a special object - the causal matrix, which describes the structure of the sign components. Operations and relationships in the sign based world model simulating of the psychological characteristics of human behaviour are determined on this basis.

Keywords:

sign based world model, sign image, sign significance, sign personal meaning, causal matrix, semiotic network, script generation, agglutination, generalization.

PP. 5-22.

References

1. Edelman G.M. Neural Darwinism: The Theory Of Neuronal Group Selection. New York: Basic Books, 1987. 400 p.
2. Friederici A.D., Singer W. Grounding language processing on basic neurophysiological principles // Trends Cogn. Sci. 2015. Vol. 19, № 6. P. 329–338.
3. Grossberg S. From brain synapses to systems for learning and memory: Object recognition, spatial navigation, timed  conditioning, and movement control // Brain Res. Elsevier, 2015. Vol. 1621. P. 270–293.
4. Gurney K., Prescott T.J., Redgrave P. A computational model of action selection in the basal ganglia. I. A new functional anatomy // Biol. Cybern. 2001. Vol. 84, № 6. P. 401–410.
5. Kahneman D. Thinking Fast and Slow. New York: Penguin, 2011. 443 p.
6. Kuznetsov S.O. Mathematical aspects of concept analysis // J. Math. Sci. 1996. Vol. 80, № 2. P. 1654–1698.
7. Kuznetsov S.O., Ob’’edkov S.A. Comparing Performance of Algorithms for Generating Concept Lattices // ICCS’01  International Workshop on Concept Lattices-based KDD. 2001. P. 35–47.
8. Loula A., Queiroz J. Synthetic Semiotics: on modelling and simulating the emergence of sign processes // AISB/IACAP World Congress 2012: Computational Philosophy, Part of Alan Turing Year 2012. Birmingham, 2012. P. 102–129.
9. Osipov G.S. Sign-based representation and word model of actor // 2016 IEEE 8th International Conference on Intelligent Systems (IS) / ed. Yager R. et al. IEEE, 2016. P. 22–26.
10. Osipov G.S. Signs-Based vs. Symbolic Models // Advances in Artificial Intelligence and Soft Computing / ed. Sidorov G., Galicia-Haro S.N. Springer International Publishing, 2015. P. 3–11.
11. Panov A.I. Behavior Planning of Intelligent Agent with Sign World Model // Biol. Inspired Cogn. Archit. 2017. Vol. 19. P.  21–31.
12. Pulvermüller F. How neurons make meaning: brain mechanisms for embodied and abstract-symbolic semantics // Trends Cogn. Sci. 2013. Vol. 17, № 9. P. 458–470.
13. Rolls E.T. A computational theory of episodic memory formation in the hippocampus // Behav. Brain Res. Elsevier B.V., 2010. Vol. 215, № 2. P. 180–196.
14. Roy D. Semiotic schemas: A framework for grounding language in action and perception // Artif. Intell. 2005. Vol. 167, № 1–2. P. 170–205.
15. Skrynnik A., Petrov A., Panov A.I. Hierarchical Temporal Memory Implementation with Explicit States Extraction // Biologically Inspired Cognitive Architectures (BICA) for Young Scientists / ed. Samsonovich A. V., Klimov V. V., Rybina G. V. Springer International Publishing, 2016. P. 219–225.
16. Stanovich K.E. Distinguishing the reflective, algorithmic, and autonomous minds: Is it time for a tri-process theory? // In  two minds: Dual processes and beyond / ed. Evans J., Frankish K. Oxford University Press, 2009. P. 55–88.
17. Asmolov G. A., Asmolov A. G. From We-Media to I-Media: Identity Transformations in the Virtual World // Psychology in Russia: State of Art. 2009. Vol. 5, no. 1. P. 101.
18. Vygotsky L. S. Thought and Language. MIT Press, 1986. P. 344.
19. Ivanitsky A. M. Brain basis of subjective experience: information synthesis hypothesis // Neuroscience and Behavioral Physiology. 1996. Vol. 46, no. 2. Pp. 251-252.
20. Ivanitsky A. M. Brain science on the way to solving the problem of consciousness // Herald of the Russian Academy of Sciences. 2010. Vol. 80, no. 3. Pp. 229-236.
21. Leontyev A. N. The Development of Mind. Kettering: Erythros Press, Media, 2009. P. 428.
22. Vinogradov A.N., Osipov G.S., Zhilyakova L.Y. Dynamic intelligent systems: I. Knowledge representation and basic  algorithms // J. Comput. Syst. Sci. Int. 2002. Vol. 41, № 6. P. 953–960.
23. Osipov G.S. Znakovye modeli kak al'ternativa simvol'nym // Gibridnye i sinergeticheskie intellektual'nye sistemy: Materialy III Vserossijskoj Pospelovskoj konferentsii s mezhdunarodnym uchastiem / pod red. А.V. Kolesnikov. : Izdatel'stvo BFU im. Immanuila Kanta, 2016. P. 56–69. (In Russian)
24. Osipov G. S. Formulation of subject domain models. 1. Heterogeneous semantic nets // Soviet Journal of Computer and Systems Sciences. 1990. Vol. 30, no. 2. Pp. 1-12.
25. Osipov G.S. Priobretenie znanij intellektual'nymi sistemami. M.: Nauka. Fizmatlit, 1997. 112 p. (In Russian)
26. Osipov G. S., Panov A. I., Chudova N. V. Behavior control as a function of consciousness. I. World model and goal setting  // Journal of Computer and Systems Sciences International. 2014. Vol. 53, no. 4. Pp. 517-529.
27. Osipov G. S., Panov A. I., Chudova N. V. Behavior Control as a Function of Consciousness. II. Synthesis of a Behavior Plan // Journal of Computer and Systems Sciences International. 2015. Vol. 54, no. 6. Pp. 882-896.
28. Panov A.I. Algebraic Properties fo Recogniton Operators in Modeling Visual Perception of Dynamic Scenes // Proceedings of International Conference IIP -10, 2014, P. 133
29. Peirce C. The Collected Papers of Charles Sanders Peirce / ed. by C. Hartshorne, P. Weiss. Harvard University Press, 1958.
30. Rubinshtejn S.L. Voobrazhenie // Osnovy obshhej psikhologii. SPb.: Izdatel'stvo «Piter», 2000. (in Russian)
31. Fillmore C. J. The Case for Case // Universals in Linguistic Theory. N. Y.: Holt, Rinehart, and Winston, 1968. P. 1-88.
32. Frege G. Translations from the Philosophical Writings of Gottlob Frege / ed. By P. T. Geach, M. Black. New York :  Philosophical Library, 1952.
33. Chudova N.V. Kontseptual'noe opisanie kartiny mira dlya zadachi modelirovaniya povedeniya, osnovannogo na soznanii // Iskusstvennyj intellekt i prinyatie reshenij. 2012. № 2. P. 51–62. (In Russian)
34. Mountcastle V.B. Perceptual Neuroscience. The Cerebral Cortex. Cambridge: Harvard University Press, 1998. 512 p.