Abstract
Original language | English |
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Title of host publication | Seventh Mexican International Conference on Artificial Intelligence (MICAI '08) |
Publisher | IEEE Explore |
Pages | 287-292 |
Number of pages | 6 |
ISBN (Print) | 978-0-7695-3441-1 |
DOIs | |
Publication status | Published - Oct 2008 |
Publication series
Name | Lecture Notes in Artificial Intelligence |
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Publisher | Springer |
Number | 5137 |
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Multi-agent ERA Model Based on Belief Solves Multi-port Container Stowage Problem. / Liu, Yanbin; Wang, Kangping; Guo, Dongwei; Pang, Wei; Zhou, Chunguang .
Seventh Mexican International Conference on Artificial Intelligence (MICAI '08). IEEE Explore, 2008. p. 287-292 (Lecture Notes in Artificial Intelligence; No. 5137).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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TY - GEN
T1 - Multi-agent ERA Model Based on Belief Solves Multi-port Container Stowage Problem
AU - Liu, Yanbin
AU - Wang, Kangping
AU - Guo, Dongwei
AU - Pang, Wei
AU - Zhou, Chunguang
PY - 2008/10
Y1 - 2008/10
N2 - ERA is the acronym of environment, reactive rules and agent; the model is a new effective multi-agent cooperation framework, which has been successfully applied in a wide range of areas. Multi-port Container Stowage (MPCS) is a more practical problem. However, multi-port makes the container stowage problem more difficult. This paper presents a MPCS model based on time series and a multi-agent ERA model based on belief to solve the multi-port container stowage problem. The algorithm introduces the belief concept into agents and creates a multi-agent ERA model. The searching direction and strength of the agents are determined by the learning machine using belief parameter. Agent has the ability to evaluate its searching path. At last, the feasibility and efficiency of the model are validated by the experimental result.
AB - ERA is the acronym of environment, reactive rules and agent; the model is a new effective multi-agent cooperation framework, which has been successfully applied in a wide range of areas. Multi-port Container Stowage (MPCS) is a more practical problem. However, multi-port makes the container stowage problem more difficult. This paper presents a MPCS model based on time series and a multi-agent ERA model based on belief to solve the multi-port container stowage problem. The algorithm introduces the belief concept into agents and creates a multi-agent ERA model. The searching direction and strength of the agents are determined by the learning machine using belief parameter. Agent has the ability to evaluate its searching path. At last, the feasibility and efficiency of the model are validated by the experimental result.
U2 - 10.1109/MICAI.2008.10
DO - 10.1109/MICAI.2008.10
M3 - Conference contribution
SN - 978-0-7695-3441-1
T3 - Lecture Notes in Artificial Intelligence
SP - 287
EP - 292
BT - Seventh Mexican International Conference on Artificial Intelligence (MICAI '08)
PB - IEEE Explore
ER -