Learning From Humans: Agent Modeling With Individual Human Behaviors

Abstract

Multiagent-based simulation (MABS) is a very active interdisciplinary area bridging multiagent research and social science. The key technology to conduct truly useful MABS is agent modeling for reproducing realistic behaviors. In order to make agent models realistic, it seems natural to learn from human behavior in the real world. The challenge presented in this paper is to obtain an individual behavior model by using participatory modeling in the traffic domain. We show a methodology that can elicit prior knowledge for explaining human driving behavior in specific environments, and then construct a driving behavior model based on the set of prior knowledge. In the real world, human drivers often perform unintentional actions, and occasionally, they have no logical reason for their actions. In these cases, we cannot rely on prior knowledge to explain them. We are forced to construct a behavior model with an insufficient amount of knowledge to reproduce the driving behavior. To construct such individual driving behavior model, we take the approach of using knowledge from others to complement the lack of knowledge from the target. To clarify that the behavior model including prior knowledge from others offers individuality in driving behavior, we experimentally confirm that the driving behaviors reproduced by the hybrid model correlate reasonably well with human behavior.

Driving Style Analysis Using Data Mining Techniques

“…There are some works that tries to determine the Driving Style Analysis Using Data Mining Techniques 655 driving style, seen as "the attitude, orientation and way of thinking for daily driving", based on questionnaires' surveys [4,5]. More recent works use a virtual driving simulator to collect realistic driving data from human drivers and to model human driving behavior [18], or classify driving style by combining objective rank method with recurrent learning based on Elman's type neural network [20]. There are also related works on modelling traffic flow, driving course decisions, or drivers behavior in emergency situations [19].…”

The next industrial revolution: Integrated services and goods

“…In geophysics, sophisticated models of large data sets are simulating seismic activities in order to better understand earthquakes(Barbot et al 2012, Segall 2012). In transportation, multiagent-based simulation is being employed to better understand driver behavior(Hattori et al 2011). Other examples of how to abstract insights and improve services using big data analytics are reviewed inLavalle et al (2011).…”

Multiagent Decision Making on Transportation Networks

“…Most work in this area focuses on adaptive control of traffic lights (e.g., Ref. [1]) and intersections [6], or on modeling individual drivers' behaviors [11]. Kim et al [15] investigate if increased penalties decrease illegal speeding.…”