Multi-agent Based Hierarchy Simulation Models of Carrier-based Aircraft Catapult Launch

With the aid of multi-agent based modeling approach to complex systems, the hierarchy simulation models of carrier-based aircraft catapult launch are developed. Ocean, carrier, aircraft, and atmosphere are treated as aggregation agents, the detailed components like catapult, landing gears, and disturbances are considered as meta-agents, which belong to their aggregation agent. Thus, the model with two layers is formed i.e. the aggregation agent layer and the meta-agent layer. The information communication among all agents is described. The meta-agents within one aggregation agent communicate with each other directly by information sharing, but the meta-agents, which belong to different aggregation agents exchange their information through the aggregation layer first, and then perceive it from the sharing environment, that is the aggregation agent. Thus, not only the hierarchy model is built, but also the environment perceived by each agent is specified. Meanwhile, the problem of balancing the independency of agent and the resource consumption brought by real-time communication within multi-agent system （MAS） is resolved. Each agent involved in carrier-based aircraft catapult launch is depicted, with considering the interaction within disturbed atmospheric environment and multiple motion bodies including carrier, aircraft, and landing gears. The models of reactive agents among them are derived based on tensors, and the perceived messages and inner frameworks of each agent are characterized. Finally, some results of a simulation instance are given. The simulation and modeling of dynamic system based on multi-agent system is of benefit to express physical concepts and logical hierarchy clearly and precisely. The system model can easily draw in kinds of other agents to achieve a precise simulation of more complex system. This modeling technique makes the complex integral dynamic equations of multibodies decompose into parallel operations of single agent, and it is convenient to expand, maintain, and reuse the program codes.

A hybrid agent⁃based machine learning method for human⁃centred energy consumption prediction

Occupant behaviour has significant impacts on the performance of machine learning algorithms when predicting building energy consumption.Due to a variety of reasons(e.g.,underperforming building energy management systems or restrictions due to privacy policies),the availability of occupational data has long been an obstacle that hinders the performance of machine learning algorithms in predicting building energy consumption.Therefore,this study proposed an agent⁃based machine learning model whereby agent⁃based modelling was employed to generate simulated occupational data as input features for machine learning algorithms for building energy consumption prediction.Boruta feature selection was also introduced in this study to select all relevant features.The results indicated that the performances of machine learning algorithms in predicting building energy consumption were significantly improved when using simulated occupational data,with even greater improvements after conducting Boruta feature selection.

基于Agent-based的森林火灾下生态系统仿真研究

为研究森林火灾对动物物种数量平衡的影响,提出一种基于Agent-based的森林火灾下多物种仿真模型。通过将森林火灾过程中各类型物种和火灾等元素抽象为主体,根据各类物种和森林火灾的真实特性,提炼出各类主体的属性和行为规则;利用ABM (agent-based model)模型可以展现复杂系统中多主体交互的特性,构建出多物种的森林生态模型和森林火灾模型,在对模型合理性进行验证的基础上整合2个模型;分析森林火灾发生前后的物种变化情况及影响。结果表明:当火灾扑灭后的恶劣环境条件下,生殖能力强且死亡率高的物种依然能够迅速繁殖后代;火灾干扰强度越高,该类物种增长越快。竞争能力强但繁殖能力弱的物种却难以在灾难中得以恢复。

技术创新难度对高科技企业最优创新投入的影响——基于Agent-Based Model的分析

通过Agent-Based Model模拟了高技术企业的发展环境,分析了不同技术创新门槛下企业技术的发展状况。研究发现,高技术创新门槛阻碍了市场最高技术水平的发展,但能提升市场平均技术水平及公司数目。同时,企业研发活动存在能实现最大技术增长的最优创新研发投入比例,并且随着技术创新门槛的提升,企业的研发政策向模仿性投入倾斜。由此可知,针对有高技术创新门槛的高科技企业,政府应提升专利保护强度以影响企业的最优研发政策,保证高新技术产业的健康发展。

Agent-Based Model: A Surging Tool to Simulate Infectious Diseases in the Immune System

Agent-based models (ABMs) are capable of constructing individual system components at different levels of representation to describe non-linear relationships between those components. Compared to a traditional mathematical modeling approach, agent-based models have an inherent spatial component with which they can easily describe local interactions and environmental heterogeneity. Furthermore, agent-based model maps interactions among agents inherently to the biological phenomenon by embedding the stochastic nature and dynamics transitions, thereby demonstrating suitability for the development of complex biological processes. Recently, an abundance of literature has presented application of agent-based modeling in the biological system. This review focuses on application of agent-based modeling to progression in simulation of infectious disease in the human immune system and discusses advantages and disadvantages of agent-based modeling application. Finally, potential implementation of agent-based modeling in relation to infectious disease modeling in future research is explored.

Emulating a System Dynamics Model with Agent-Based Models: A Methodological Case Study in Simulation of Diabetes Progression

An agent-based simulation model hierarchy emulating disease states and behaviors critical to progression of diabetes type 2 was designed and implemented in the DEVS framework. This model was built to approximately reproduce some essential findings that were previously reported for a rather complex model of diabetes progression. Our models are translations of basicelements of this previously reported system dynamics model of diabetes. The system dynamics model, which mimics diabetes progression over an aggregated US population, was disaggregated and reconstructed bottom-up at the individual (agent) level. Four levels of model complexity were defined in order to systematically evaluate which parameters are needed to mimic outputs of the system dynamics model. The four estimated models attempted to replicate stock counts representing disease states in the system dynamics model while estimating impacts of an elderliness factor, obesity factor and health-related behavioral parameters. Health-related behavior was modeled as a simple realization of the Theory of Planned Behavior, a joint function of individual attitude and diffusion of social norms that spread over each agent’s social network. Although the most complex agent-based simulation model contained 31 adjustable parameters, all models were considerably less complex than the system dynamics model which required numerous time series inputs to make its predictions. All three elaborations of the baseline model provided significantly improved fits to the output of the system dynamics model, although behavioral factors appeared to contribute more than the elderliness factor. The results illustrate a promising approach to translate complex system dynamics models into agent-based model alternatives that are both conceptually simpler and capable of capturing main effects of complex local agent-agent interactions.

A Combined Discrete Event—Agent Based Approach to Modeling Tensile Strength of One-Dimensional Fibrous Materials. Face Validation and Effect of the Basic Model Parameters

A combined method of discrete event and agent based modelling has been applied to the computer modelling and simulation of the tensile strength of one-dimensional fibrous materials (ODFM). This combined method is based on the concept of discrete event simulation as being applied to the modeling of the structure of the fiber flow and on the concept of agent based modelling for modelling and simulation of the fiber interaction within the structure of the fibrous material. Frictional and traction forces arise as the result of this fiber interaction. A model of the ODFM tensile strength, which is based on the slippage effect, is created and studied in this research. Only frictional and traction forces determine the tensile strength in this kind of the model. The article examines the validation problem of the slippage effect based tensile strength model and questions regarding the strength potential estimation through variation in the parameters of the model.

An Agent-Based Model for Studying the Impact of Herd Mobility on the Spread of Vector-Borne Diseases: The Case of Rift Valley Fever (Ferlo Senegal)

Vector-borne diseases are highly sensitive to environment and to environmental changes. Rift Valley Fever (RFV) is a mosquito-borne zootic virus associated with severe diseases in human beings and economic consequences to livestock sector. Animal and human movements have a fundamental impact on RVF transmission. In recent years, there has been a growing interest in the use of mathematics and agent based models to represent and analyze the dynamic of RFV transmission. However, no previous study has taken into consideration animal herds’ mobility and precipitation factors to understand the disease spread. This limitation underlines the necessity to use computational model approach based on multi-agent system in the study of vector-borne diseases transmission and diffusion. In this paper, a multi-agent system combining conceptual model expressiveness is used to study animal herds’ mobility and the precipitation parameter impact on the Rift Valley Fever outbreak in Ferlo Barkedji in Northern Senegal. Simulation scenarios with various parameters, including rain quality, hosts, vectors, camp dispersal around ponds, etc., are unrolled. The different results we have obtained show that the evolution of the number of infected hosts and infected vectors depend on the degree of animal herds’ mobility and on precipitations. Our model provides a framework that permits predicting the spread of the disease associated with the mobility of animal herds.

Intelligent Agent Based Mapping of Software Requirement Specification to Design Model

Automatically mapping a requirement specification to design model in Software Engineering is an open complex problem. Existing methods use a complex manual process that use the knowledge from the requirement specification/modeling and the design, and try to find a good match between them. The key task done by designers is to convert a natural language based requirement specification (or corresponding UML based representation) into a predominantly computer language based design model—thus the process is very complex as there is a very large gap between our natural language and computer language. Moreover, this is not just a simple language conversion, but rather a complex knowledge conversion that can lead to meaningful design implementation. In this paper, we describe an automated method to map Requirement Model to Design Model and thus automate/partially automate the Structured Design (SD) process. We believe, this is the first logical step in mapping a more complex requirement specification to design model. We call it IRTDM (Intelligent Agent based requirement model to design model mapping). The main theme of IRTDM is to use some AI (Artificial Intelligence) based algorithms, semantic representation using Ontology or Predicate Logic, design structures using some well known design framework and Machine Learning algorithms for learning over time. Semantics help convert natural language based requirement specification (and associated UML representation) into high level design model followed by mapping to design structures. AI method can also be used to convert high level design structures into lower level design which then can be refined further by some manual and/or semi automated process. We emphasize that automation is one of the key ways to minimize the software cost, and is very important for all, especially, for the “Design for the Bottom 90% People” or BOP (Base of the Pyramid People).

A New Formula for Partitions in a Set of Entities into Empty and Nonempty Subsets, and Its Application to Stochastic and Agent-Based Computational Models

In combinatorics, a Stirling number of the second kind S (n,k)? is the number of ways to partition a set of n objects into k nonempty subsets. The empty subsets are also added in the models presented in the article in order to describe properly the absence of the corresponding type i of state in the system, i.e. when its “share” Pi =0?. Accordingly, a new equation for partitions P (N, m)? in a set of entities into both empty and nonempty subsets was derived. The indistinguishableness of particles (N identical atoms or molecules) makes only sense within a cluster (subset) with the size?0≤ni ≥N. The first-order phase transition is indeed the case of transitions, for example in the simplest interpretation, from completely liquid state?typeL = {n1 =N, n2 = 0} to the completely crystalline state??typeC= {n1 =0, n2 = N }. These partitions are well distinguished from the physical point of view, so they are ‘typed’ differently in the model. Finally, the present developments in the physics of complex systems, in particular the structural relaxation of super-cooled liquids and glasses, are discussed by using such stochastic cluster-based models.

Agent-Based Modeling: An Application to Natural Resource Management

Computer programs have been categorized as a useful tool to evaluate the complexity of systems. In fact, agent-based modeling (ABM) is considered a new method to model complex systems characterized by the role of independent and interrelating agents. Simulations contribute in estimating and comprehending emerging behaviors that require the development of new regulations for local agents that would make improvements to the system. This paper offers an example of a methodology and a process utilized to develop a simulation model named Befergyonet, an ABM used to conduct computer simulations within a spatio-intertemporal environment. The methodology discussed in this paper is intended solely to stimulate the use of innovative computer programs to simulate complex systems as an approach to represent real world events and may be a methodological guide for readers interested in developing their own ABM.

On the Use of Agent Based Modelling for Addressing the Social Component of Urban Water Management in Europe

The paper aimed to provide a review of different tools that estimate how human behavior changes by water management strategies and quantify this change to support the decisions of urban water managers. To support decision makers, it is essential to be able to model the urban water system’s human part explicitly and link it to the hydro system’s response, rather than only explore the reaction of the system based on scenarios. To do so, tools are needed that can model the human part of the system, explore its reaction to potential changes and dynamically link back this to the techno-environmental model of the water system. This work reviews state-of-the-art ABMs that are publicly available focusing on the human part of the urban water system in Europe. The review leads to the proposals of three pillars for future development of ABMs for urban water management in Europe: end-user enablement;Machine Learning and Artificial Intelligence integration and adversaries modelling.

基于主体基建模(ABM)的中国智能制造装备产业演化研究

智能制造装备面向的是具有复杂决策机制的制造企业,因此其产业演化规律相较于其他新兴产业有更强的特殊性。目前中国智能制造装备的市场份额较少、缺乏竞争优势,智能制造装备企业难以实现可持续发展。因此,在关键核心技术短期无法突破场景下,探讨政府高效推动智能制造装备市场结构变化、对智能制造装备产业演化的作用机制。通过问卷调查收集智能制造装备市场真实数据,在主体基建模方法(ABM)框架下构建国内外智能制造装备企业决策模型与制造企业决策模型,模拟智能制造装备产业演化过程,分析智能制造装备企业数量、市场需求数量和市场需求分布等市场结构核心参量变化特征,并进一步探究不同扶持政策对产业演化的作用效果。结果发现:在智能制造装备产业演化过程中,市场需求结构的变化是改变国产智能制造装备企业市场份额的关键要素,国产智能制造装备企业市场需求数量经历“增长—下跌—再增长”3个阶段,市场需求在空间上呈“分散—集聚—扩散”分布;在无外部力量干预的情境下,国产智能制造装备企业的市场份额存在显著的持续下降趋势,无论是面向市场需求数量还是面向产品质量提升的政策措施都有利于提升国产智能制造装备企业在产业中的市场状况;不同的政策策略对产业演化进程产生的效果不同,面向产品质量提升的政策对提升国产智能制造装备企业的市场份额效果更显著。

Multi-agent decision support system for missile defense based on improved PSO algorithm

Ballistic missile defense system (BMDS) is important for its special role in ensuring national security and maintaining strategic balance. Research on modeling and simulation of the BMDS beforehand is essential as developing a real one requires lots of manpower and resources. BMDS is a typical complex system for its nonlinear, adaptive and uncertainty characteristics. The agent-based modeling method is well suited for the complex system whose overall behaviors are determined by interactions among individual elements. A multi-agent decision support system (DSS), which includes missile agent, radar agent and command center agent, is established based on the studies of structure and function of BMDS. Considering the constraints brought by radar, intercept missile, offensive missile and commander, the objective function of DSS is established. In order to dynamically generate the optimal interception plan, the variable neighborhood negative selection particle swarm optimization (VNNSPSO) algorithm is proposed to support the decision making of DSS. The proposed algorithm is compared with the standard PSO, constriction factor PSO (CFPSO), inertia weight linear decrease PSO (LDPSO), variable neighborhood PSO (VNPSO) algorithm from the aspects of convergence rate, iteration number, average fitness value and standard deviation. The simulation results verify the efficiency of the proposed algorithm. The multi-agent DSS is developed through the Repast simulation platform and the constructed DSS can generate intercept plans automatically and support three-dimensional dynamic display of missile defense process.

An agent-based simulation with NetLogo platform to evaluate forward osmosis process(PRO Mode)

Forward osmosis(FO), as an emerging technology, is influenced by different factors such as operating conditions,module characteristics, and membrane properties. The general aim of this study was to develop a suitable(flexible,comprehensive, and convenient to use) computational tool which is able to simulate osmosis through an asymmetric membrane oriented in pressure retarded osmosis(PRO) mode in a wide variety of scenarios. For this purpose, an agent-based model was created in NetLogo platform, which is an easy-to-use application environment with graphical visualization abilities and well suited for modeling a complex system evolving over time. The simulation results were validated with empirical data obtained from literature and a great agreement was observed. The effect of various parameters on process performance was investigated in terms of temperature,cross-flow velocity, length of the module, pure water permeability coefficient, and structural parameter of the membrane. Results demonstrated that the increase in all parameters, except structural parameter of the membrane and the length of module led to the increase of average water flux. Moreover, nine different draw solutes were selected in order to assess the influence of net bulk osmotic pressure difference between the draw solution(DS) and feed solution(FS)(known as the driving force of FO process) on water flux. Based on the findings of this paper, the performance of FO process(PRO mode) can be efficiently evaluated using the NetL ogo platform.

An Integrated Agent-Based Framework for Assessing Air Pollution Impacts

Air pollution has considerable impact on human health and the wellbeing. Thus many regions of the world have established air pollution standards to ensure a minimum level of air quality. Precise assessment of the health and socio-economic impacts of air pollution is, however, a complex task;indeed, methods based within an epidemiological tradition generally underestimate human risk of exposure to polluted air. In this study, we introduce an agent-based modeling approach to ascertaining the impact of changes in particulate matter (PM10) on mortality and frequency of hospital visits in the greater metropolitan region of Sydney, Australia. Our modeling approach simulates human movement and behavioral patterns in order to obtain an accurate estimate of individual exposure to a pollutant. Results of our analysis indicate that a 50% reduction in PM10 levels (relative to the baseline) could considerably lower mortality, respiratory hospital admissions and emergency room visits leading to reduced pressure on health care sector costs and placing lower stress on emergency medical facilities. Our analysis also highlights the continued need to avoid significant increases in air pollution in Sydney so that associated health impacts, including health care costs, do not increase.

A Cautionary Note on the Application of GIS in Spatial Optimization Modeling

Spatial optimization as part of spatial modeling has been facilitated significantly by integration with GIS techniques. However, for certain research topics, applying standard GIS techniques may create problems which require attention. This paper serves as a cautionary note to demonstrate two problems associated with applying GIS in spatial optimization, using a capacitated p-median facility location optimization problem as an example. The first problem involves errors in interpolating spatial variations of travel costs from using kriging, a common set of techniques for raster files. The second problem is inaccuracy in routing performed on a graph directly created from polyline shapefiles, a common vector file type. While revealing these problems, the paper also suggests remedies. Specifically, interpolation errors can be eliminated by using agent-based spatial modeling while the inaccuracy in routing can be improved through altering the graph topology by splitting the long edges of the shapefile. These issues suggest the need for caution in applying GIS in spatial optimization study.

Carbon Tax and Renewable Energy Diffusion in the Deregulated Texas Electricity Market: An Agent-Based Analysis

In the United States, emission regulations are enacted at a state level;individual states are allowed to define what methods they will use to mitigate their carbon emissions. The consequence of this is especially interesting in the state of Texas where new legislation has created a “deregulated” electricity market in which end-users are capable of choosing their electricity provider and subsequently the type of electricity they wish to consume (generated by fossil fuels or renewable sources). In this paper we analyze the effects of carbon tax on the development of renewable generation capacity at the utility level while taking into account expected adoption of rooftop PV systems by individual consumers using agent based modeling techniques. Monte Carlo simulations show carbon abatement trends and proffer updated renewable portfolio standards at various levels of likelihood.

融合ABM与博弈论的武汉市城镇开发边界划定与优化

城镇开发边界的划定与优化工作一直是国土空间规划改革的重要组成部分,其有助于遏制城市盲目扩张,促进资源结构有序重组。文章以武汉市为研究对象,从模拟角度探索城镇开发边界的划定与优化方法。考虑不同微观主体的偏好与目标,将多智能体模型(ABM)与元胞自动机(CA)耦合,初步划定城镇开发边界,在此基础上,引入基于博弈论的城市开发生态冲突耦合协调机制,实现划定结果的优化。研究表明:基于ABM的城镇开发边界划定方法可以深度刻画微观层面的智能体决策行为,且不同收入水平的居民智能体具有不同的偏好与目标;优化后城市用地面积为519.32 km2,新增城市用地主要集中在中心城区边缘,呈点状填充式扩展,且主城区外已出现了一定数量的卫星城,如黄陂前川、新洲邾城等。文章的研究从开发与保护的视角出发,旨在为国土空间开发格局优化提供参考。