Modeling and Analysis of Autonomous Agents’ Decisions in Learning to Cross a Cellular Automaton-Based Highway
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 a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-09-01
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| Series: | Computation |
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| Online Access: | https://www.mdpi.com/2079-3197/7/3/53 |
| _version_ | 1811198807893868544 |
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| author | Shengkun Xie Anna T. Lawniczak Chong Gan |
| author_facet | Shengkun Xie Anna T. Lawniczak Chong Gan |
| author_sort | Shengkun Xie |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T01:37:32Z |
| format | Article |
| id | doaj.art-a98b0a5912f54fc8b9ada5fc0e6e5c0a |
| institution | Directory Open Access Journal |
| issn | 2079-3197 |
| language | English |
| last_indexed | 2024-04-12T01:37:32Z |
| publishDate | 2019-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computation |
| spelling | doaj.art-a98b0a5912f54fc8b9ada5fc0e6e5c0a2022-12-22T03:53:16ZengMDPI AGComputation2079-31972019-09-01735310.3390/computation7030053computation7030053Modeling and Analysis of Autonomous Agents’ Decisions in Learning to Cross a Cellular Automaton-Based HighwayShengkun Xie0Anna T. Lawniczak1Chong Gan2Global Management Studies, Ted Rogers School of Management, Ryerson University, Toronto, ON M5B 2K3, CanadaDepartment of Mathematics and Statistics, University of Guelph, Guelph, ON N1G 2W1, CanadaDepartment of Mathematics and Statistics, University of Guelph, Guelph, ON N1G 2W1, CanadaFor 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.https://www.mdpi.com/2079-3197/7/3/53autonomous agentsknowledge base transferagents’ decision-makingcanonical correlation analysisregression tree |
| spellingShingle | Shengkun Xie Anna T. Lawniczak Chong Gan Modeling and Analysis of Autonomous Agents’ Decisions in Learning to Cross a Cellular Automaton-Based Highway Computation autonomous agents knowledge base transfer agents’ decision-making canonical correlation analysis regression tree |
| title | Modeling and Analysis of Autonomous Agents’ Decisions in Learning to Cross a Cellular Automaton-Based Highway |
| title_full | Modeling and Analysis of Autonomous Agents’ Decisions in Learning to Cross a Cellular Automaton-Based Highway |
| title_fullStr | Modeling and Analysis of Autonomous Agents’ Decisions in Learning to Cross a Cellular Automaton-Based Highway |
| title_full_unstemmed | Modeling and Analysis of Autonomous Agents’ Decisions in Learning to Cross a Cellular Automaton-Based Highway |
| title_short | Modeling and Analysis of Autonomous Agents’ Decisions in Learning to Cross a Cellular Automaton-Based Highway |
| title_sort | modeling and analysis of autonomous agents decisions in learning to cross a cellular automaton based highway |
| topic | autonomous agents knowledge base transfer agents’ decision-making canonical correlation analysis regression tree |
| url | https://www.mdpi.com/2079-3197/7/3/53 |
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