Abstract
A lot of effort has been devoted to mathematical modelling and simulation of complex systems for a better understanding of their dynamics and control. Modelling and analysis of computer simulations outcomes are also important aspects of studying the behaviour of complex systems. It often involves the use of both traditional and modern statistical approaches, including multiple linear regression, generalized linear model and non-linear regression models such as artificial neural networks. In this work, we first conduct a simulation study of the agents’ decisions learning to cross a cellular automaton based highway and then, we model the simulation data using artificial neural networks. Our research shows that artificial neural networks are capable of capturing the functional relationships between input and output variables of our simulation experiments, and they outperform the classical modelling approaches. The variable importance measure techniques can consistently identify the most dominant factors that affect the response variables, which help us to better understand how the decision-making by the autonomous agents is affected by the input factors. The significance of this work is in extending the investigations of complex systems from mathematical modelling and computer simulations to the analysis and modelling of the data obtained from the simulations using advanced statistical models.
Highlights
Robotic science and technology are playing increasingly an essential role in human society, and they are helping and improving the quality of our lives in many different ways, from transportation to robotic surgery [1,2,3,4]
In [15], we have investigated the interconnections between the experimental setups and the considered output variables correct crossing decisions (CCDs), incorrect crossing decisions (ICDs), correct waiting decisions (CWDs), and incorrect waiting decisions (IWDs), using canonical correlation analysis (CCA)
These measures are displayed for artificial neural networks (ANNs) models, respectively, with output variables CCD, ICD, CWD and IWD in Figures 6 and 7
Summary
Robotic science and technology are playing increasingly an essential role in human society, and they are helping and improving the quality of our lives in many different ways, from transportation to robotic surgery [1,2,3,4]. One may consider replacing sophisticated robots with many simpler robots that are less complex but which can still perform tasks of the complex robots [5]. In achieving some tasks, a swarm of robots may be more flexible than a single complex robot. By sharing their knowledge and learning experience from each other within a swarm, the robots may learn faster how to perform some tasks in dynamically changing environments. Given these attributes a swarm of robots may perform some tasks more reliably, effectively and efficiently
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