EcoSimNet: A Multi-Agent System for Ecological Simulation and Optimization

被引:0
作者
Pereira, Antonio [1 ]
Reis, Luis Paulo [1 ]
Duarte, Pedro [2 ]
机构
[1] Univ Porto, Fac Engn, DEI FEUP, NIAD&R LIACC, Rua Dr Roberto Frias S-N, P-4200465 Oporto, Portugal
[2] Univ Fernado Pessoa, CIAGEG UFP, P-4249004 Porto, Portugal
来源
PROGRESS IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS | 2009年 / 5816卷
关键词
Ecological Simulations; Agent-Based Applications; Multi-Agent Simulation and Modelling; Optimization; Parallel Simulated Annealing;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Ecological models may be very complex due to the large number of physical, chemical, biological processes and variables and their interactions, leading to long simulation times. These models may be used to analyse different management scenarios providing support to decision-makers. Thus, the simultaneous simulation of different scenarios can make the process of analysis and decision quicker, provided that there are mechanisms to accelerate the generation of new scenarios and optimization of the choices between the results presented. This paper presents a new simulation platform - EcoSimNet - specially designed for environmental simulations, which allows the inclusion of intelligent agents and the introduction of parallel simulators to speed Lip and optimize the decision-making processes. Experiments were performed using EcoSimNet computational platform with an agent controlling several simulators and implementing a parallel version of the simulated annealing algorithm for optimizing aquaculture production. These experiments showed the capabilities of the framework, enabling a fast optimization process and making this work it step forward towards an agent based decision support system to optimize complex environmental problems.
引用
收藏
页码:473 / +
页数:3
相关论文
共 19 条
[1]  
[Anonymous], ARTIFICIAL INTELLIGE
[2]  
[Anonymous], 1998, REINFORCEMENT LEARNI
[3]  
Cruz F, 2007, LECT NOTES ARTIF INT, V4874, P593
[4]  
Duarte CA, 2007, COMPUT METH APPL SCI, V5, P1
[5]  
Duarte P, 2005, NATO SCI S SS IV EAR, V47, P121
[6]   Mathematical modelling to assess the carrying capacity for multi-species culture within coastal waters [J].
Duarte, P ;
Meneses, R ;
Hawkins, AJS ;
Zhu, M ;
Fang, J ;
Grant, J .
ECOLOGICAL MODELLING, 2003, 168 (1-2) :109-143
[7]   Applications of symbolic machine learning to ecological modelling [J].
Dzeroski, S .
ECOLOGICAL MODELLING, 2001, 146 (1-3) :263-273
[8]   FUTURE PATHS FOR INTEGER PROGRAMMING AND LINKS TO ARTIFICIAL-INTELLIGENCE [J].
GLOVER, F .
COMPUTERS & OPERATIONS RESEARCH, 1986, 13 (05) :533-549
[9]  
Holland J.H., 1975, Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, DOI 10.7551/mitpress/1090.001.0001
[10]  
Huhns MN, 1999, MULTIAGENT SYSTEMS, P79