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)


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.


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



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.


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Mathematics and StatisticsUniversity of GuelphGuelphCanada

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