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Agent Having Quantum Properties: The Superposition States and the Entanglement

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10448)

Abstract

In agent-based simulation and modelling of intelligent complex systems, the problem of decision making by agents having incomplete, uncertain, local or global, exchanged or observed information is very common. Recent studies on quantum cognition introduce in the decision process modelling and analysis, quantum properties such as superposition state, non-locality, oscillation, interference or entanglement. This paper proposes a model of quantum-like agents able to implement quantum properties of superposition state and local or non-local entanglement. A case study based on an adaptation of the Takuzu game illustrates our proposed approach of quantum agents modelling. A discussion on the interest of decomposing or not components of a system in the intelligent complex systems modelling is also proposed.

Keywords

Quantum agent Quantum-like model Superposition states Entanglement Artificial intelligence Agent-based modelling 

References

  1. 1.
    Liu, J., Zhang, S.W.: Characterizing web usage regularities with information foraging agents. IEEE Trans. Knowl. Data Eng. 40, 7478–7491 (2004)Google Scholar
  2. 2.
    Kazemifard, M., Ghasem-Aghaee, N., Koenig, B.L., Ören, T.I.: An emotion understanding framework for intelligent agents based on episodic and semantic memories. Auton. Agent. Multi-Agent Syst. 28(1), 126–153 (2014)CrossRefGoogle Scholar
  3. 3.
    Scheepers, C., Engelbrecht, A.P.: Training multi-agent teams from zero knowledge with the competitive coevolutionary team-based particle swarm optimizer. Soft. Comput. 20(2), 607–620 (2016)CrossRefGoogle Scholar
  4. 4.
    Khrennikov, A.Y.: Ubiquitous Quantum Structure: From Psychology to Finance. Springer, Berlin (2010)CrossRefGoogle Scholar
  5. 5.
    Wang, Z., Busemeyer, J.R., Atmanspacher, H., Pothos, E.M.: The potential of using quantum theory to build models of cognition. Top. Cogn. Sci. 5(4), 672–688 (2013)Google Scholar
  6. 6.
    Fuss, L., Navarro, D.: Open, parallel, cooperative and competitive decision processes: a potential provenance for quantum probability decision models. Top. Cogn. Sci. 5(4), 818–843 (2013)Google Scholar
  7. 7.
    Fougères, A.-J.: Towards quantum agents: the superposition state property. Int. J. Comput. Sci. Issues 13(5), 20–27 (2016)CrossRefGoogle Scholar
  8. 8.
    Aerts, D.: Quantum structure in cognition. J. Math. Psychol. 53, 314–348 (2009)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Bruza, P.D., Busemeyer, J., Gabora, L.: Introduction to the special issue on quantum cognition. J. Math. Psychol. 53, 303–305 (2009)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Busemeyer, J.R., Bruza, P.D.: Quantum Models of Cognition and Decision. Cambridge University Press, Cambridge (2012)CrossRefGoogle Scholar
  11. 11.
    Aerts, D., Sozzo, S., Gabora, L., Veloz, T.: Quantum structure in cognition: fundamentals and applications. In: Proceedings of the Fifth International Conference on Quantum, Nano and Micro Technologies (ICQNM 2011), Nice, France, 21–27 August 2011Google Scholar
  12. 12.
    Wang, Z., Solloway, T., Shiffrin, R.M., Busemeyer, J.R.: Context effects produced by question orders reveal quantum nature of human judgments. PNAS 111(26), 9431–9436 (2014)CrossRefGoogle Scholar
  13. 13.
    Bohm, D., Hiley, B.J.: Non-locality and locality in the stochastic interpretation of quantum mechanics. Phys. Rep. 172(3), 93–122 (1989)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Einstein, A., Podolsky, B., Rosen, N.: Can quantum mechanical description of reality be considered complete? Phys. Rev. 47, 777–780 (1935)CrossRefGoogle Scholar
  15. 15.
    Vértesi, T., Brunner, N.: Quantum nonlocality does not imply entanglement distillability. Phys. Rev. Lett. 108(3), 030403 (2012)CrossRefGoogle Scholar
  16. 16.
    Horodecki, R., Horodecki, P., Horodecki, M., Horodecki, K.: Quantum entanglement. Rev. Mod. Phys. 81(2), 865 (2009)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Weiss, G.: Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence. MIT Press, Cambridge (1999)Google Scholar
  18. 18.
    Fougères, A.-J.: Modelling and simulation of complex systems: an approach based on multi-level agents. Int. J. Comput. Sci. Issues 8(6), 8–17 (2011)Google Scholar
  19. 19.
    Odell, J.: Agent technology what is it and why do we care? Enterp. Archit. 10(3), 1–25 (2007). Executive report, Cutter Consortium, Arlington, MAGoogle Scholar
  20. 20.
    Wooldridge, M.: Agent-based software engineering. IEE Proc. Softw. Eng. 144(1), 26–37 (1997)CrossRefGoogle Scholar
  21. 21.
    Jennings, N.R.: On agent-based software engineering. Artif. Intell. 117, 277–296 (2000)CrossRefGoogle Scholar
  22. 22.
    Biswas, P.K.: Towards an agent-oriented approach to conceptualization. Appl. Soft Comput. 8(1), 127–139 (2008)CrossRefGoogle Scholar
  23. 23.
    Fougères, A.-J.: A modelling approach based on fuzzy agents. Int. J. Comput. Sci. Issues 9(6), 19–28 (2013)Google Scholar
  24. 24.
    Jennings, N.R., Sycara, K., Wooldridge, M.: A roadmap of agent research and development. Auton. Agents Multi-Agent Syst. 1(1), 7–38 (1998)CrossRefGoogle Scholar
  25. 25.
    Macal, C.M., North, M.J.: Tutorial on agent-based modelling and simulation. J. Simul. 4, 151–162 (2010)CrossRefGoogle Scholar
  26. 26.
    Nielsen, M.A., Chuang, I.: Quantum Computation and Quantum Information. Cambridge University Press, Cambridge (2000)MATHGoogle Scholar
  27. 27.
    Zhang, W.R.: G-CPT symmetry of quantum emergence and submergence—an information conservational multiagent cellular automata unification of CPT symmetry and CP violation for equilibrium-based many-world causal analysis of quantum coherence and decoherence. J. Quantum Inf. Sci. 6, 62–97 (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.ESTA Lab’, ESTA, School of Business and EngineeringBelfortFrance

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