Creature Learning to Cross a CA Simulated Road

Ławniczak, Anna T.; Ernst, Jason; Stefano, Bruno N. Di

doi:10.1007/978-3-642-33350-7_44

Cited by 15 publications

(9 citation statements)

References 4 publications

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“…The model of autonomous agents' learning to cross cellular automaton (CA)-based highway is a fully discrete algorithmic model, and it was developed by Lawniczak, Di Stefano, Ernst, and Yu [16][17][18][19][20][21][22]. It simulates the epitome of an actual vehicle driving and crossing environment and decision-making process through four main components: highway, vehicles, agents, and their decisions.…”

Section: Simulation Model Descriptionmentioning

confidence: 99%

“…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%

“…where the terms v(Desire) and v(Fear), respectively, are the values of an active agent's desire and fear parameters. The main simulation loop of the model of cognitive agents learning to cross a CA-based highway consists of the following steps [16][17][18][19][20][21][22]…”

Section: Simulation Model Descriptionmentioning

confidence: 99%

“…All of these factors motivated us to conduct a simulation study of cognitive agents learning to cross a cellular automaton-based highway, developed by Lawniczak, Di Stefano, Ernst, and Yu [16][17][18][19][20][21][22] and its modeling problem of the data coming from the simulation. Within this modeling and simulation study, the cognitive agents are abstractions of simple robots, which are generated at a crossing point of the highway.…”

Section: Introductionmentioning

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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Section: Simulation Model Descriptionmentioning

confidence: 99%

Section: Simulation Model Descriptionmentioning

confidence: 99%

Section: Simulation Model Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

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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“…The presented work is an extansion of our study on modeling naïve creature crossing a highway presented in [8], [9] and [10].…”

Section: Introductionmentioning

confidence: 99%

Improved performance of naïve creature learning to cross a highway

Ławniczak

Ernst

Stefano³

2014

2014 IEEE 27th Canadian Conference on Electrical and Computer Engineering (CCECE)

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We present a model of simulated naïve creatures learning to cross a highway. The creatures are able to learn by "imitating what works and avoiding what does not work". Our simulations show that the creatures' population success in learning to cross a single-lane unidirectional highway is influenced by the conditions of the environment; i.e. by the car traffic density, presence or not of "erratic drivers", and by the selection of a crossing point location on the highway. The creatures' population success of crossing the highway can be improved by allowing the creatures to move randomly along the edge of the highway to find a safer location to cross from, and by preserving the knowledge base from environment with lower car traffic density to the one with higher car traffic density.

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

Ławniczak

Xie

et al. 2016

Lecture Notes in Computer Science

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Creature Learning to Cross a CA Simulated Road

Cited by 15 publications

References 4 publications

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

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

Improved performance of naïve creature learning to cross a highway

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

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Creature Learning to Cross a CA Simulated Road

Cited by 15 publications

References 4 publications

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

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

Improved performance of na&#x00EF;ve creature learning to cross a highway

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

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Improved performance of naïve creature learning to cross a highway