Modelling Cities as a Collection of TeraSystems – Computational Challenges in Multi-Agent Approach

Landowner characteristics influence his/her willingness to change landuse practices to provide more or less environmental benefits. However, most studies of agricultural/environmental polices identify landowners as homogenous. And, the primary cause of failure of many environmental and other polices is the lack of knowledge on how humans may respond to polices based on changes in their behavior (Stern, 1993). From socioeconomic theory and empirical research, landowners can be identified as individuals who make agricultural landuse decisions independently based on their objectives. Identifying possible classes of landowners, assessing how each would potentially respond to policy alternatives, and the resulting pattern of land uses in a watershed or a riparian corridor would be very useful to policy makers as they evaluated alternatives. Agricultural landscapes are important producers of ecosystem services. The mix of ecosystem services and commodity outputs of an agricultural landscape depends on the spatial pattern of land uses emerging from individual land use decisions. However, many empirical studies show that the production of ecosystem services from agricultural landscapes is declining. This is consistent with research conducted over the last few decades showing there is a narrow range of social circumstances under which landowners are willing to make investments in the present to achieve public benefits in the future through investing in natural capital resulting in public goods which are frequently produced as ecosystem services. In this study an agent-based model within a watershed planning context is used to analyze the tradeoffs involved in producing a number of ecosystem services and agricultural commodities given price and policy scenarios while assuming three different types of agents in terms of their goals. The agents represent landowners who have been divided into a number of different groups based on their goals and the size of their farm operations. The multi-agent-based model is developed using a heuristic search and optimization technique called genetic algorithm (GA) (Holland), which belongs to a broader class of evolutionary algorithms. GAs exhibit three properties (1) they start with a population of solution, (2) they explore the solution space through recombination and mutation and (3) they evaluate individual solutions based on their appropriate fitness value(s), for example given profit maximizing agents this would be gross margin. A GA is a heuristic stochastic search and optimization method, which works by mimicking the evolutionary principles and chromosomal processing in natural genetics. The three economic agents that are modeled are based on variations in their objective functions and constraints. This study will help in identifying the tradeoffs associated with various agents in the provision of ecosystem services and agricultural commodities. The agent model developed here will help policy and decision maker identify the various agents within the watershed and assess various policy options based on that information. The study will also help to understand the interaction and feedback between the agents and their environment associated with various policy initiatives. The results of the study indicate that the agent model correctly predicts the actual landuse landcover map by 75 percent. Key words: Multifunctional agriculture, Agent based modeling, Genetic Algorithm.

The prosperity of modern human civilization is attributed to the huge amount of resources and energy. With the increasing population and technological advancements, thedemand for energy will definitely continue to increase. Howto save energy has become an important issue. In this work, we proposed a system to collect the electricity usage data in campus buildings through smart meters and environmental sensors, and process the huge amount of data by big data processing techniques. Therefore, we introduced cloud computing and big data processing architecture as solutions to build a real-time energy monitoring system for smart campus. In this work, we used Hadoop ecosystem which is built on big data processing architecture to improve the capacity of big data storage and processing for our system. The proposedsystem has been implemented in Tunghai University. Finally, the system interface vividly displays the electricity usage states in campus buildings; thus, users can monitor the electricity usage in the campus and historical data at any time and any place.

In the past, data processing was completed on a computer or a server. This is because the resources available at that time were limited. However, with the progress of technology, it is possible to use multiple computers and servers to process large amounts of data. This is where cloud computing comes into play. Cloud computing allows users to access data from anywhere at any time using an Internet connection. It also reduces the cost of enterprises because they do not have to buy additional hardware or software for each user. Aiming at the problems of single scale of traditional computer data processing and poor degree of adaptive parallel computing, this paper proposes a multi-source information accelerated data processing method of cloud computing technology, analyzes the application of cloud computing technology in computer data processing, and adopts the dynamic optimization technology of cloud computing speed to build a dynamic adjustment model of multi-source information data flow application of cloud computing, so as to reduce the energy cost of cloud computing, Combined with the experience of data processing users, the model can support multi-user computing entity sharing in the cloud to improve the running efficiency of data flow applications and reduce the energy cost of computer data processing. This article first outlines the basic meaning of cloud computing technology, starting with providing conditions for data transmission and sharing, and highlighting the application value of cloud computing technology. It analyzes the trend of cloud computing technology, and combines the modeling of cloud computing technology in computer data processing to propose application countermeasures for cloud computing technology in computer data processing.

This article presents an approach to modelling the socio-economic impacts of migration using an agent-based model (ABM) of interactions between migrants and natives. The model also accounts for a regulatory function of government which is the centralized creation of new workplaces that differ in the level of ‘technological return’ (i.e. the labour productivity that depends on the sectoral belonging of the formed workplaces). The proposed approach is based on the previously developed model of interactions between migrants and native individuals. It is focused on studying the socio-economic impacts of migration in the system with a more complex regulatory function of the government, which creates low-technological and high-technological workplaces that are attractive for migrants and natives, respectively. The agent-government has two possible strategies of workplace creation: cluster-based workplace creation in areas with high concentration of migrants and natives and creation of uniform workplaces aimed at increasing multi-particle interactions between agents of different types, and reducing the level of population segregation. This study also investigates the processes of assimilation, which are subject to the level of segregation of the studied communities, public investment in education and integration, etc. The proposed model also considers the influence of various control parameters, in particular, the influence of the agents’ tolerance level on their location choice in a boundary neighbourhood, the influence of the agents’ education level on the job search area dimension, and other important characteristics reflecting the behavioural features of members of the studied communities. Socio-economic impacts of migration are studied under various scenario conditions, which include different patterns of agents’ behaviour belonging to the considered communities, the rate of new migrants’ inflow, the amount of government education expenditures, etc.

Intelligent agents have been regarded as a new notion to build complex software systems. In this paper, we propose an agent-based software development process based on Belief-Desire-Intention (BDI) agent model as a new software development process. The Belief-Desire-Intention (BDI) model has been used as a fundamental ingredient to the new agent-based modeling method. In our agent system proposed here, each agent is made flesh by assigning its own belief, desire and intention. Here we have shown a seamless software development modeling technique consistently based on the BDI model. Even though there are many valuable arguments to define agents beside the BDI model, we currently use the BDI model only to simplify the agent modeling because we try to develop a more realistic agent-based modeling technique. We will extend this work to support other key concepts in agent computing.Here we propose a new approach comparing it with our previous approach to support the BDI agent-based modeling techniques. The previous approach finds intentions, desires and beliefs from two different kinds of use cases - external use cases and internal use cases - and supporting tools. The new approach finds in sequence desires, intentions and beliefs by using not only different kinds of use cases, sequence diagram, activity diagrams, and dataflow diagrams. To prove the usability of our software development process, we also provide a case study to clarify the description of our BDI agent software development process. This paper also introduces a brief structure of a CASE tool that we are currently developing to support the BDI agent software development process.

NoSQL databases are designed to meet the huge data storage requirements of cloud computing and big data processing. NoSQL databases have lots of advanced features in addition to the conventional RDBMS features. Hence, the “NoSQL” databases are popularly known as “Not only SQL” databases. A variety of NoSQL databases having different features to deal with exponentially growing data-intensive applications are available with open source and proprietary option. This chapter discusses some of the popular NoSQL databases and their features on the light of CAP theorem.

A Cyber-physical system (CPS) is an engineering system made of computational components, i.e. cyber elements, and physical elements, that are connected by a communication network. CPSs have emerged as the contemporarily leading technology in major industry sectors such as manufacture, aerospace, automotive, etc. Nowadays CPS is almost the synonym of control systems for large and complex engineering systems. In addition, CPSs have inevitably interweaved with new technologies like Internet of Things, cloud computing, ubiquitous computing, and big data processing. Taming the complexity has been the key challenge in CPS design. A novel declarative computing based platform was proposed in our previous paper to unify modeling and design of both cyber and physical components in CPSs. In this paper, the concepts and principles of the proposed declarative platform are depicted in details. In addition, modeling techniques of declarative networking and declarative control are showcased with concrete simulation examples.

The race among manufacturers to build convenient, safe, and autonomous Connected Cars by applying the latest digital technologies, and ultimately a completely self-driving vehicle, is already underway. One of the cornerstones of such vehicles is the continuous ingestion of massive amounts of data from a wide variety of hardware components, including sensors, onboard cameras, and further external sources. Cloud computing and big data processing are ideal candidates and already proven technologies to store and process the heterogeneous, rapidly growing, and large-scale data sets. The cloud may act as a kind of central hub or as an Internet of Things (IoT) back-end where the sensor and the other available data can be gathered while also offering an elastic platform where the vast amount of data can be processed, analyzed and distributed real-time. In our paper we detail the evolution of a cloud-based, scalable IoT back-end framework and services built on top for handling and processing vehicular data in various use case scenarios: CAN data collection, remote device flashing, Eco-driving, weather report and forecast. The first version is an Infrastructure-as-a-Service (IaaS) solution with a reference implementation deployed on an OpenNebula based cloud. The second iteration runs on a private Platform-as-a-Service (PaaS) cloud built on the Cloud Foundry platform within the premises of an automotive supplier company. Both variants have been successfully evaluated and validated with benchmarks.

A multiagent design for self-healing in electric power distribution systems

Summary Substance flow analysis (SFA) is a frequently used industrial ecology technique for studying societal metal flows, but it is limited in its ability to inform us about future developments in metal flow patterns and how we can affect them. Equation‐based simulation modeling techniques, such as dynamic SFA and system dynamics, can usefully complement static SFA studies in this respect, but they are also restricted in several ways. The objective of this article is to demonstrate the ability of agent‐based modeling to overcome these limitations and its usefulness as a tool for studying societal metal flow systems. The body of the article summarizes the parallel implementation of two models—an agent‐based model and a system dynamics model—both addressing the following research question: What conditions foster the development of a closed‐loop flow network for metals in mobile phones? The results from in silico experimentation with these models highlight three important differences between agent‐based modeling (ABM) and equation‐based modeling (EBM) techniques. An analysis of how these differences affected the insights that could be extracted from the constructed models points to several key advantages of ABM in the study of metal flow systems. In particular, this analysis suggests that a key advantage of the ABM technique is its flexibility to enable the representation of societal metal flow systems in a more native manner. This added flexibility endows modelers with enhanced leverage to identify options for steering metal flows and opens new opportunities for using the metaphor of an ecosystem to understand metal flow systems more fully.

Agent-based modelling of wind damage processes and patterns in forests

Understanding complex socio-economic systems is a key problem for commercial organizations. In this chapter we discuss the use of agent-based modelling to produce decision support tools to enhance this understanding. We consider the important aspects of the model creation process which include the facilitation of dialogue necessary to extract knowledge, the building of understanding, and the identification of model limitations. It is these aspects that are crucial in the establishment of trust in a model. We use the example of modelling opinion diffusion within a customer population and its effect on product adoption to illustrate how the agent-based modelling technique can be an ideal tool to create models of complex socioeconomic systems. We consider the advantages compared to alternative, more conventional approaches available to analysts and management decision makers.

Advances on information technology in the past decades have provided new tools to assist scientists in the study of social and natural phenomena. Agent-based modeling techniques have flourished recently, encouraging the introduction of computer simulations to examine behavioral patterns in complex human and biological systems. Real-world social dynamics are very complex, containing billions of interacting individuals and an important amount of data (both spatial and social). Dealing with large-scale agent-based models is not an easy task and encounters several challenges. The design of strategies to overcome these challenges represents an opportunity for high performance parallel and distributed implementation. This chapter examines the most relevant aspects to deal with large-scale agent-based simulations in social sciences and revises the developments to confront technological issues.

In today’s rapidly evolving digital landscape, the explosion of data volume and complexity has necessitated the development of advanced processing frameworks that can scale effectively. Apache Spark emerges as a leading technology in big data processing, offering powerful in-memory computing capabilities and streamlined analytical operations. This paper explores the utilization of Spark for scaling data pipelines and performing comprehensive analytics across diverse datasets. By distributing computational tasks across clusters, Spark significantly reduces processing time and enhances real-time data analysis, making it indispensable for modern enterprises. The study details how Spark integrates with existing data architectures, supporting a broad spectrum of applications ranging from batch processing to interactive analytics. In addition, Spark’s robust ecosystem—featuring libraries for machine learning, graph processing, and stream analysis—enables organizations to address complex data challenges efficiently. Key case studies illustrate successful deployments, emphasizing improvements in performance, fault tolerance, and cost-effectiveness. The research further examines techniques for optimizing Spark configurations to achieve maximum throughput and minimal latency, highlighting best practices for resource management in both on-premises and cloud environments. Ultimately, this paper demonstrates that by leveraging Apache Spark, organizations can transform raw data into actionable insights, drive innovation, and maintain a competitive edge in a data-centric world. Furthermore, the framework’s scalability allows for seamless integration of emerging technologies, ensuring adaptability to evolving business requirements. Future research directions include enhancing security measures, reducing energy consumption, and further refining analytical accuracy. These advancements will continue to empower data scientists and engineers in solving intricate data challenges while setting industry benchmarks.

In probabilistic risk assessment (PRA) of nuclear power plants (NPPs), human reliability analysis (HRA) is conducted to identify potential human failure events that could contribute to risk scenarios and estimate human error probabilities. Lessons learned from the 2011 Fukushima Daiichi NPP accident underscored that for PRA, it is critical to model external control room (Ex-CR) human actions. The state-of-practice HRA methods, historically developed for the main control room HRA, are limited in capturing the unique nature of Ex-CR human actions, such as location dependence (in addition to the time dependence) of human actions and spatiotemporal interactions of human performance with the surrounding physical environments, for instance, hazard propagation. To advance the Ex-CR HRA in the context of the fire PRA for NPPs, the authors’ team developed a simulation-based fire crew performance model using an agent-based modeling (ABM) technique. The ABM fire crew simulation was coupled with a fire progression model through a spatiotemporal interface using a geographic information system. This paper focuses on the validation of the ABM simulation, which is the key requirement for the simulation-based Ex-CR human performance model to be utilized in PRA. The existing validation approach, initially developed for physical models in the fire PRA of NPPs, is extended for validation of the simulation-based Ex-CR human performance model. Model uncertainty is used as a measure of model validity, which facilitates the incorporation of the validation result into the PRA. The degree of the model uncertainty is characterized by a lognormal error model whose parameters are quantified based on a pairwise comparison between empirical data and model predictions. The proposed validation approach is demonstrated using a case study of the fire PRA of NPPs. This study makes two research contributions: (1) it is the first to validate the simulation-based Ex-CR human performance model against empirical human performance data and incorporate the validation result into the PRA of NPPs, and (2) this study, for the first time, conducts a controlled experimental test to collect empirical data for fire crew performance at NPPs.