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

Author(s):  
Fa Zhang ◽  
Shi-Hui Wu ◽  
Zhi-Hua Song

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.

Author(s):  
Marisa Faggini ◽  
Bruna Bruno ◽  
Anna Parziale

AbstractFollowing the reverse engineering (RE) approach to analyse an economic complex system is to infer how its underlying mechanism works. The main factors that condition the difficulty of RE are the number of variable components in the system and, most importantly, the interdependence of components on one another and nonlinear dynamics. All those aspects characterize the economic complex systems within which economic agents make their choices. Economic complex systems are adopted in RE science, and they could be used to understand, predict and model the dynamics of the complex systems that enable to define and to control the economic environment. With the RE approach, economic data could be used to peek into the internal workings of the economic complex system, providing information about its underling nonlinear dynamics. The idea of this paper arises from the aim to deepen the comprehension of this approach and to highlight the potential implementation of tools and methodologies based on it to treat economic complex systems. An overview of the literature about the RE is presented, by focusing on the definition and on the state of the art of the research, and then we consider two potential tools that could translate the methodological issues of RE by evidencing advantages and disadvantages for economic analysis: the recurrence analysis and the agent-based model (ABM).


2018 ◽  
Vol 7 (1) ◽  
pp. 5-24 ◽  
Author(s):  
Martina Husáková

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.


2010 ◽  
Vol 2 (2) ◽  
pp. 49-63
Author(s):  
Ahmad Esmaeili ◽  
Nasser Mozayani

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.


Author(s):  
Volkhard Klinger ◽  
Arne Klauke

Realizing a nerve signal based prostheses control or limb stimulation is a great challenge in medical technology. It requires a recording and an identification process of the motion-based action potentials of motor and sensory nerves within the corresponding neural bundle. Two additional key factors are used by multi agent-based learning algorithm: The anatomical disposition of the nerves within the neural bundle and the inverse kinematic. In this paper the authors introduce the Smart Modular Biosignal Acquisition, Identification and Control System and its application environment. They present the different process levels and their characteristic identification contribution and they give an overview of the multi-agent based identification framework. The authors show the verification environment and present results regarding the first-level identification procedure.


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