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JOURNALS // Artificial Intelligence and Decision Making // Archive

Artificial Intelligence and Decision Making, 2017 Issue 4, Pages 5–22 (Mi iipr261)

This article is cited in 9 papers

Cognitive modeling

Relationships and operations in agent's sign-based model of the world

G. S. Osipov, A. I. Panov

Institute for Systems Analysis of Russian Academy of Sciences

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.


 English version:
, 2018, 45:5, 317–330

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© Steklov Math. Inst. of RAS, 2024