Intelligent hybrid multi-agent computational architecture for complex systems

2002 ◽  
Author(s):  
R. Khosla
Keyword(s):  
Complex Systems ◽  
Computational Architecture ◽  
Multi Agent

scholarly journals Use of the Multi-Agent Paradigm in Sustainable Tourism

2018 ◽  
Vol 7 (1) ◽  
pp. 5-24 ◽  
Author(s):  
Martina Husáková
Keyword(s):  
Complex Systems ◽  
Sustainable Tourism ◽  
Multi Agent Systems ◽  
Economic Prosperity ◽  
Cultural Economic ◽  
Bottom Up ◽  
Agent Based ◽  
Agent Systems ◽  
Multi Agent ◽  
In Silico Models

Abstract Complex systems are characterised by a huge amount of components, which are highly linked with each other. Tourism is one of the examples of complex systems collecting various activities leading to the enrichment of travellers in the view of receiving new experiences and increasing economic prosperity of specific destinations. The complex systems can be investigated with various bottom-up and top-down approaches. The multi-agent-based modelling is the bottom-up approach that is focused on the representation of individual entities for the exploration of possible interactions among them and their effects on surrounding environments. These systems are able to integrate knowledge of socio-cultural, economic, physical, biological or environmental systems for in-silico models development, which can be used for experimentation with a system. The main aim of the presented text is to introduce links between tourism, complexity and to advocate usefulness of the multi-agent-based systems for the exploration of tourism and its sustainability. The evaluation of suitability of the multi-agent systems in tourism is based on the investigation of fundamental characteristics of these two systems and on the review of specific applications of the multi-agent systems in sustainable tourism.

HoloJade

2010 ◽  
Vol 2 (2) ◽  
pp. 49-63
Author(s):  
Ahmad Esmaeili ◽  
Nasser Mozayani
Keyword(s):  
Complex Systems ◽  
Multi Agent Systems ◽  
Levels Of Abstraction ◽  
Meta Model ◽  
Agent Systems ◽  
Generic Solution ◽  
Multi Agent ◽  
Different Levels

Holonic Multi-Agent Systems (HMAS) provide a convenient and relevant way to analyze, model, and simulate complex systems in which a large number of entities are interacting at different levels of abstraction. Many models have been proposed for the implementation of these systems; however, most are not general enough to cover applications other than the ones for which they are applied. In this paper, the authors introduce HoloJade, an extension to JADE platform, as a generic solution for the development of HMASs in which Holons and their assigned roles are presented as first level entities available at runtime. This includes a detailed description of the extension, in terms of its meta-model, the needed protocols for the possible interactions, and facilities for the reorganization of the holons. In this paper, the authors also present a hypothetical library example to demonstrate the steps for designing a holonic structure using this extension.

scholarly journals Framework of Experimental Design and Data Mining in Multi-agent Simulation

2022 ◽  
pp. 1-7
Author(s):  
Fa Zhang ◽  
Shi-Hui Wu ◽  
Zhi-Hua Song
Keyword(s):  
Complex System ◽  
Data Mining ◽  
Experimental Design ◽  
Complex Systems ◽  
Key Factors ◽  
Agent Based ◽  
Meta Model ◽  
Reduction Methods ◽  
Multi Agent ◽  
Using Data

Multi-agent based simulation (MABS) is an important approach for studying complex systems. The Agent-based model often contains many parameters, these parameters are usually not independent, with differences in their range, and may be subjected to constraints. How to use MABS investigating complex systems effectively is still a challenge. The common tasks of MABS include: summarizing the macroscopic patterns of the system, identifying key factors, establishing a meta-model, and optimization. We proposed a framework of experimental design and data mining for MABS. In the framework, method of experimental design is used to generate experiment points in the parameter space, then generate simulation data, and finally using data mining techniques to analyze data. With this framework, we could explore and analyze complex system iteratively. Using central composite discrepancy (CCD) as measure of uniformity, we designed an algorithm of experimental design in which parameters could meet any constraints. We discussed the relationship between tasks of complex system simulation and data mining, such as using cluster analysis to classify the macro patterns of the system, and using CART, PCA, ICA and other dimensionality reduction methods to identify key factors, using linear regression, stepwise regression, SVM, neural network, etc. to build the meta-model of the system. This framework integrates MABS with experimental design and data mining to provide a reference for complex system exploration and analysis.

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