Elements of Conceptual Thinking in Planning the Behavior of Autonomous Intelligent Agents

The expediency of using the tools of visual-effective, visual-figurative and conceptual thinking for planning the purposeful activity of autonomous intelligent agents in problem environments of various degrees of a priori uncertainty has been substantiated. The content is revealed and the role of each form of thinking is shown in the process of automatic planning of the purposeful behavior of autonomous intelligent agents in the changing conditions of functioning. The special role of conceptual thinking in the performance of complex tasks by autonomous agents and the planning of polyphasic behavior associated with it is indicated. Taking into account the complexity of the problems associated with the formalization of mental acts of conceptual thinking, possible ways of its gradual development from the initial level to the transition to higher levels of development are shown, expanding on this basis the class of tasks solved by autonomous intelligent agents. A model of knowledge representation and tools for deriving solutions of the initial level of conceptual thinking have been developed, which allow intelligent agents to break down the tasks they receive into sub-goals of behavior. Then, on this basis, plan polyphase activity by searching for solutions to the associated subtasks, which ensure the determination of the minimum length routes of movement in a prob lematic environment with obstacles and the purposeful manipulation of objects in it. The tools are synthesized allowing to establish the order of elaboration of complex actions included in the structure of the task formulated by autonomous intelligent agents. It is shown that the further development of the proposed methodological foundations for constructing intelligent problem sol vers is associated with the formalization of a higher level of mental acts of conceptual thinking, which make it possible to solve practical problems of different complexity, formulated both in procedural and declarative form of presentation in the form of various target situations of the problem environment, having a large dimension.

1. Podd’yakov N. N. Preschooler thinking, Moscow, Pedagogika, 1977, 277 p. (in Russian).

2. Pospelov D. A. Modeling reasoning. Experience in the analysis of mental acts, Moscow, Radio i svyaz’, 1989, 184 p. (in Russian).

3. Bershtejn L. S., Melekhin V. B. Planning the behavior of an intelligent robot, Moscow, Enegoatomizdat, 1994, 40 p. (in Russian).

4. Stuart Russell, Peter Norvig. Artificial Intelligence: A Modern Approach. 4 ed. Pearson, 2020, 1216 p.

5. George F. Luger. Artificial Intelligence. Structures and Strategies for Complex Problem Solving. 6 ed. Pearson, 2008, 784 p.

6. Kelly A. Mobile Robotics: Mathematics, Models, and Methods, Cambridge, Cambridge University Press, 2013. 808 p.

7. Vagin V. N., Golovina E.YU., Zagoryanskaya A. A., Fomina M. V. Reliable and plausible conclusion in intelligent systems, Moscow, Fizmatlit, 2008, 729 p. (in Russian).

8. Melekhin V. B. Replenishment of knowledge of an autonomous intelligent robot in underdetermined environments based on combined rules for deriving the logic of conditionally dependent reasoning, Iskusstvennyj Intellekt i Prinyatie Reshenij, 2016, no 3, pp. 11—20.

9. Moris Koen, Ernst Nagel’. An introduction to logic and the scientific method, Moscow, Socium, 2017. 520 p. (in Russian).

10. Melekhin V. B., Hachumov V. M. Replenishment of knowledge of an autonomous unmanned quadrocopter-manipulator in an underdetermined problematic environment, Iskusstvennyj Intellekt i Prinyatie Reshenij, 2019, no 1, pp. 39—48 (in Russian).

11. Melekhin V. B., Hachumov M. V. Fuzzy semantic networks as an adaptive model for representing knowledge of autonomous intelligent systems, Iskusstvennyj Intellekt i Prinyatie Reshenij, 2020, no 3, pp. 1—7 (in Russian).

12. Kulinich A. A., Karpov V. E., Karpova I. P. Social communities of robots, Moscow, URSS, OOO "LENAND", 2019, 352 p. (in Russian).

13. Melekhin V. B. Model of representation and acquisition of new knowledge by an autonomous intelligent robot based on the logic of conditionally dependent predicates, Izvestiya Rossijskoj Akademii Nauk. Teoriya i Sistemy Upravleniya, 2019, no. 5, pp. 87—107 (in Russian).

14. Polyakov I. V., Chepovskij A. A., Chepovskij A. M. Algorithms for finding paths on large graphs, Fundamental’naya i Prikladnaya Matematika, 2014, no. 1(19), pp. 165—17 (in Russian).

15. Esposito F., Paola D., Matta I. On Distributed Virtual Network Embedding with Guarantees, IEEE ACM Trans. Netw, 2014, Vol. 4(1), pp. 569—582.

16. Cormen T., Leiserson S., Rivest R. Introduction to Algorithms, Cambridge, 1990, 1091 p.

17. Dehmer M., Emmert-Streib F. ed. Quantitative Graph Theory. Mathematical Foundations and Applications, Taylor & Francis, 2015, 493 p.

18. Vekker L. M. Psyche and role, Moscow, Mysl’, 1998, 684 p. (in Russian).