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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Weng Xiao Xiong Zhu Xue Feng |
| Copyright Year | 2005 |
| Description | Author affiliation: Dept. of Traffic Eng., South China Univ. of Technol., Guangzhou, China (Weng Xiao Xiong) |
| Abstract | Contractor net protocol (CNP) and case based reasoning (CBR) are wide-application distributed problem solving methods. The paper presents an interacted learning strategy based CNP and CBR to solve non-structural, nonlinear, and complex urban traffic control decision-making under multiagent system architecture. The manager agent (MA) decomposes a regional control task into several separable subtasks which single traffic agent (TA) could deal with independently and their specifications by knowledge with CBR. MA sends task announcement messages to selected agents and requires them finish bid document in expiration-time. MA analyzes bid messages, eliminates those conflict control schemes, and optimizes their specification and resend to TAs. Through finite cycles of interacted learning, MAS could get a group of best coordinating TAs and optimal control schema. Extended KQML is a tool to realize the distributed decision-making. |
| Starting Page | 893 |
| Ending Page | 897 |
| File Size | 1342730 |
| Page Count | 5 |
| File Format | |
| ISBN | 0780391373 |
| DOI | 10.1109/ICCA.2005.1528248 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2005-06-26 |
| Publisher Place | Hungary |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Problem-solving Contracts Protocols Traffic control Decision making Multiagent systems Knowledge management IEEE news Optimal control Distributed decision making |
| Content Type | Text |
| Resource Type | Article |
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