By Topic

A Designing Method of Simulation Software for Chinese Train Control System Based on Hybrid Software Agent Model

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Xiao-hui Hu ; School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an 710072, China; School of Information and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China. E-MAIL: hxh ; Xing-she Zhou ; Jian-wu Dang

Chinese train control system (CTCS) is one of real-time distributed supervising and control systems that have the distributed physical entities needing cooperation to accomplish their local goal, make possible decisions, execute actions, negotiate through the communication protocol to reach global criterion. For designing CTCS simulation software, the hybrid software agent model that is adapted to other distributed supervising and control systems is developed on the basis of deliberative and reactive agent concepts and we discuss the interesting characteristics of this model. The proposed designing method for CTCS level 4 as MAS mainly involves building an ontology model by using UML, specifying interaction protocols formally by Petri net and determining the real-time and deliberative behavioral rules based on the ontology. The reactive memory is introduced to the model for guaranteeing to respond urgent events in real-time. The deliberative part of agent model enables it to do complex tasks based on agent's mental states and input events

Published in:

2006 International Conference on Machine Learning and Cybernetics

Date of Conference:

13-16 Aug. 2006