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Spatial Reasoning and Planning in Sign-Based World Model

  • Gleb Kiselev
  • Alexey Kovalev
  • Aleksandr I. PanovEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 934)

Abstract

The paper discusses the interaction between methods of modeling reasoning and behavior planning in a sign-based world model for the task of synthesizing a hierarchical plan of relocation. Such interaction is represented by the formalism of intelligent rule-based dynamic systems in the form of alternate use of transition functions (planning) and closure functions (reasoning). Particular attention is paid to the ways of information representation of the object spatial relationships on the local map and the methods of organizing pseudo-physical reasoning in a sign-based world model. The paper presents a number of model experiments on the relocation of a cognitive agent in different environments and replenishment of the state description by means of the variants of logical inference.

Keywords

Sign Sign-based world model Relocation planning Reasoning modeling Pseudo-physical logic 

Notes

Acknowledgements

This work was supported by Russian Foundation for Basic Research (Project No. 18-07-01011 and 17-29-07051).

References

  1. 1.
    Osipov, G.S., Panov, A.I., Chudova, N.V.: Behavior control as a function of consciousness. I. World model and goal setting. J. Comput. Syst. Sci. Int. 53, 517–529 (2014)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Osipov, G.S., Panov, A.I., Chudova, N.V.: Behavior control as a function of consciousness. II. Synthesis of a behavior plan. J. Comput. Syst. Sci. Int. 54, 882–896 (2015)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Alford, R., Shivashankar, V., Roberts, M., Frank, J., Aha, D.W.: Hierarchical planning: relating task and goal decomposition with task sharing. In: IJCAI International Joint Conference on Artificial Intelligence, pp. 3022–3028 (2016)Google Scholar
  4. 4.
    Stefanuk, V.L.: Dynamic expert systems. KYBERNETES Int. J. Syst. Cybern. 29(5/6), 702–709 (2000)CrossRefGoogle Scholar
  5. 5.
    Vinogradov, A.N., Osipov, G.S., Zhilyakova, L.Y.: Dynamic intelligent systems: I. Knowledge representation and basic algorithms. J. Comput. Syst. Sci. Int. 41, 953–960 (2002)Google Scholar
  6. 6.
    Osipov, G.S.: Limit behaviour of dynamic rule-based systems. Inf. Theor. Appl. 15, 115–119 (2008)Google Scholar
  7. 7.
    Pospelov, D.A., Osipov, G.S.: Knowledge in semiotic models. In: Proceedings of the Second Workshop on Applied Semiotics, Seventh International Conference on Artificial Intelligence and Information-Control Systems of Robots (AIICSR97), Bratislava, pp. 1–12 (1997)Google Scholar
  8. 8.
    Gemignani, G., Capobianco, R., Bastianelli, E., Bloisi, D.D., Iocchi, L., Nardi, D.: Living with robots: interactive environmental knowledge acquisition. Robot. Auton. Syst. 78, 1–16 (2016).  https://doi.org/10.1016/j.robot.2015.11.001CrossRefGoogle Scholar
  9. 9.
    Galindo, C., Saffiotti, A., Coradeschi, S., Buschka, P., Fern, J.A., Gonz, J.: Multi-hierarchical semantic maps for mobile robotics. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (2005)Google Scholar
  10. 10.
    Zender, H., Mozos, O.M., Jensfelt, P., Kruijff, G.M., Burgard, W.: Conceptual spatial representations for indoor mobile robots. Robot. Auton. Syst. 56, 493–502 (2008).  https://doi.org/10.1016/j.robot.2008.03.007CrossRefGoogle Scholar
  11. 11.
    Kaelbling, L.P., Lozano-Prez, T.: Integrated task and motion planning in belief space. Int. J. Robot. Res. 32(9–10), 1194–1227 (2013)CrossRefGoogle Scholar
  12. 12.
    Garrett, C.R., Lozano-Prez, T., Kaelbling, L.P.: Backward-forward search for manipulation planning. In: IEEE International Conference on Intelligent Robots and Systems, (grant 1420927), pp. 6366–6373, December 2015Google Scholar
  13. 13.
    Erdem, U.M., Hasselmo, M.E.: A biologically inspired hierarchical goal directed navigation model. J. Physiol. Paris 108(1), 28–37 (2014).  https://doi.org/10.1016/j.jphysparis.2013.07.002CrossRefGoogle Scholar
  14. 14.
    Milford, M., Wyeth, G.: Persistent navigation and mapping using a biologically inspired slam system. Int. J. Robot. Res. 29(9), 1131–1153 (2010).  https://doi.org/10.1177/0278364909340592CrossRefGoogle Scholar
  15. 15.
    Milford, M., Schulz, R.: Principles of goal-directed spatial robot navigation in biomimetic models. Philos. Trans. Roy. Soc. B: Biol. Sci. 369(1655), 20130484–20130484 (2014).  https://doi.org/10.1098/rstb.2013.0484CrossRefGoogle Scholar
  16. 16.
    Osipov, G.S.: Sign-based representation and word model of actor. In: Yager, R., Sgurev, V., Hadjiski, M., and Jotsov, V. (eds.) 2016 IEEE 8th International Conference on Intelligent Systems (IS), pp. 22–26. IEEE (2016)Google Scholar
  17. 17.
    Panov, A.I.: Behavior planning of intelligent agent with sign world model. Biol. Inspired Cogn. Archit. 19, 21–31 (2017)MathSciNetGoogle Scholar
  18. 18.
    Kiselev, G.A., Panov, A.I.: Sign-based approach to the task of role distribution in the coalition of cognitive agents. SPIIRAS Proc. 57, 161–187 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Gleb Kiselev
    • 1
    • 2
  • Alexey Kovalev
    • 2
  • Aleksandr I. Panov
    • 1
    • 3
    Email author
  1. 1.Federal Research Center “Computer Science and Control” of the Russian Academy of SciencesMoscowRussia
  2. 2.National Research University Higher School of EconomicsMoscowRussia
  3. 3.Moscow Institute of Physics and TechnologyMoscowRussia

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