Advertisement

Analysis of Rates of Agents’ Decisions in Learning to Cross a Highway in Populations with Risk Takers and Risk Avoiders

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11115)

Abstract

The rates of cognitive agents’ correct and incorrect crossing decisions, correct and incorrect waiting decisions in learning to cross cellular automaton based highway are studied. The effects of presence of risk takers and risk avoiders on these rates are investigated for agents using observational social learning strategies. One of these strategies is based on the assessment of agents crossing decisions, and another one is based on the assessment of agents crossing and waiting decisions. Also, the effects of transfer of agents’ knowledge base built in one traffic environment to the agents in another one on the rates of agents’ various decisions are investigated.

Keywords

Agents Cognitive agents Observational learning Knowledge base Decision-making Autonomous robots 

Notes

Acknowledgments

The authors acknowledge the useful discussions with B. Di Stefano, Leslie Ly and H. Wu. A. T. L. acknowledges partial financial support from the NSERC of Canada.

References

  1. 1.
    Russell, S., Norvig, P.: Artificial Intelligence, A Modern Approach. Pearson Education Limited, London (2014)zbMATHGoogle Scholar
  2. 2.
    Nehavin, C., Dautenhahn, K.: Imitation and Social Learning in Robots, Humans and Animals. Cambridge University Press, Cambridge (2007)Google Scholar
  3. 3.
    Lawniczak, A.T., Ernst, J.B., Di Stefano, B.N.: Creature learning to cross a CA simulated road. In: Sirakoulis, G.C., Bandini, S. (eds.) ACRI 2012. LNCS, vol. 7495, pp. 425–433. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-33350-7_44CrossRefGoogle Scholar
  4. 4.
    Lawniczak, A.T., Di Stefano, B.N., Ly, L., Xie, S.: Performance of population of naïve creatures with fear and desire capable of observational social learning. Acta Phys. Pol. Ser. B, Proc. Suppl. 9(1), 95–107 (2016)CrossRefGoogle Scholar
  5. 5.
    Lawniczak, A.T., Ly, L., Yu, F., Xie, S.: Effects of model parameter interactions on Naïve creatures’ success of learning to cross a highway. In: The 2016 IEEE Congress on Evolutionary Computation (IEEE CEC 2016) at IEEE WCCI 2016, 10 p. (2016)Google Scholar
  6. 6.
    Lawniczak, A.T., Yu, F.: Comparison of agent’s performance in learning to cross a highway for two decisions formulas. In: Proceedings of the 9th International Conference on Agents and Artificial Intelligence, ICAART 2017, vol. 1, pp. 208–219 (2017)Google Scholar
  7. 7.
    Nagel, K., Schreckenberg, M.: A cellular automaton model for freeway traffic. J. Phys. I 2, 2221–2229 (1992)Google Scholar
  8. 8.
    Dean, A., Voss, D.: Design and Analysis of Experiments. Springer, New York (1999).  https://doi.org/10.1007/b97673CrossRefzbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Mathematics and StatisticsUniversity of GuelphGuelphCanada

Personalised recommendations