Effects of Agents’ Fear, Desire and Knowledge on Their Success When Crossing a CA Based Highway

Ławniczak, Anna T.; Ly, Leslie; Xie, Shengkun; Yu, Fei

doi:10.1007/978-3-319-44365-2_44

Cited by 2 publications

(2 citation statements)

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“…The diagram of the abstraction of the highway, vehicles, and agents crossing the CA-based highway implemented in the model is displayed in Figure 1. For more information about the model description and previous statistical analysis of it simulation data, the reader is referred to References [16][17][18][19][20][21][22][35][36][37][38][39][40][41]. The highway and the vehicular traffic are modeled by the Nagel-Schreckenberg highway traffic model [42], which is a CA-based model consisting of a collection of cells, with each cell representing a segment of a highway of 7.5 m in length [42].…”

Section: Simulation Model Descriptionmentioning

confidence: 99%

“…For each set-up of the simulation model parameters' values, each simulation run is of a duration of 1511 time steps and it is repeated 30 times. We selected 30 repeats based on our previous work [20][21][22][35][36][37][38][39][40][41], which showed that the natural variation of simulations was captured sufficiently well by this number of repeats. For the presented study, we selected the data at the simulation end as our object of study.…”

Section: Simulation Model Descriptionmentioning

confidence: 99%

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Modeling and Analysis of Autonomous Agents’ Decisions in Learning to Cross a Cellular Automaton-Based Highway

2019

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For a better understanding of the nature of complex systems modeling, computer simulations and the analysis of the resulting data are major tools which can be applied. In this paper, we study a statistical modeling problem of data coming from a simulation model that investigates the correctness of autonomous agents’ decisions in learning to cross a cellular automaton-based highway. The goal is a better understanding of cognitive agents’ performance in learning to cross a cellular automaton-based highway with different traffic density. We investigate the effects of parameters’ values of the simulation model (e.g., knowledge base transfer, car creation probability, agents’ fear and desire to cross the highway) and their interactions on cognitive agents’ decisions (i.e., correct crossing decisions, incorrect crossing decisions, correct waiting decisions, and incorrect waiting decisions). We firstly utilize canonical correlation analysis (CCA) to see if all the considered parameters’ values and decision types are significantly statistically correlated, so that no considered dependent variables or independent variables (i.e., decision types and configuration parameters, respectively) can be omitted from the simulation model in potential future studies. After CCA, we then use the regression tree method to explore the effects of model configuration parameters’ values on the agents’ decisions. In particular, we focus on the discussion of the effects of the knowledge base transfer, which is a key factor in the investigation on how accumulated knowledge/information about the agents’ performance in one traffic environment affects the agents’ learning outcomes in another traffic environment. This factor affects the cognitive agents’ decision-making abilities in a major way in a new traffic environment where the cognitive agents start learning from existing accumulated knowledge/information about their performance in an environment with different traffic density. The obtained results provide us with a better understanding of how cognitive agents learn to cross the highway, i.e., how the knowledge base transfer as a factor affects the experimental outcomes. Furthermore, the proposed methodology can become useful in modeling and analyzing data coming from other computer simulation models and can provide an approach for better understanding a factor or treatment effect.

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