Copyright © 2012 Shiquan Zhong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

SOAR is a cognitive architecture named from state, operator and result, which is adopted to portray the drivers' guidance compliance behavior on variable message sign (VMS) in this paper. VMS represents traffic conditions to drivers by three colors: red, yellow, and green. Based on the multiagent platform, SOAR is introduced to design the agent with the detailed description of the working memory, long-term memory, decision cycle, and learning mechanism. With the fixed decision cycle, agent transforms state through four kinds of operators, including choosing route directly, changing the driving goal, changing the temper of driver, and changing the road condition of prediction. The agent learns from the process of state transformation by chunking and reinforcement learning. Finally, computerized simulation program is used to study the guidance compliance behavior. Experiments are simulated many times under given simulation network and conditions. The result, including the comparison between guidance and no guidance, the state transition times, and average chunking times are analyzed to further study the laws of guidance compliance and learning mechanism.