Abstract
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.
Highlights
Artificial intelligence and machine learning techniques are widely used in the analysis of many real-world complex systems coming from manufacturing, business, information technology, and engineering, to name a few among many others [1,2,3]
It is important to identify if there is a significant correlation between the considered factors and the assessments of decisions
We assumed that random deceleration (RD) = 0 and horizontal movement (HM) = 1, and we considered four response variables, correct crossing decision (CCD), incorrect crossing decision (ICD), correct waiting decision (CWD), and incorrect waiting decision (IWD), which denote the cumulative number of CCDs, ICDs, CWDs, and IWDs, respectively, recorded for each run at the final simulation time T = 1511
Summary
Artificial intelligence and machine learning techniques are widely used in the analysis of many real-world complex systems coming from manufacturing, business, information technology, and engineering, to name a few among many others [1,2,3]. They are important techniques that aim at Computation 2019, 7, 53; doi:10.3390/computation7030053 www.mdpi.com/journal/computation. Data obtained from the real-world observational study is not able to provide us with the causal inference for the regression model that is used for describing the response variables [8]. To be able to better understand the nature of complex systems, a computer simulation of complex systems becomes an important approach
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