Agent-based analysis and simulation of meta-reasoning processes in strategic naval planning

This paper presents analysis and simulation of meta-reasoning processes based on an agent-based meta-level architecture for strategic reasoning in naval planning. The architecture was designed as a generic agent model and instantiated with decision knowledge acquired from naval domain experts and was specified as an executable agent-based model which has been used to perform a number of simulations. To evaluate the simulation results, relevant properties for the planning decision were identified and formalized. These properties have been validated for the simulation traces.     

Two distribution methods for multiagent traffic simulations

Modeling and simulation play an important role in transportation networks analysis. With the widespread use of personalized real-time information sources, the behavior of the simulation depends heavily on individual travelers reactions to the received information. As a consequence, it is relevant for the simulation model to be individual-centered, and multiagent simulation is the most promising paradigm in this context. However, representing the movements of realistic numbers of travelers within reasonable execution times requires significant computational resources. It also requires relevant methods, architectures and algorithms that respect the characteristics of transportation networks. In this paper, we define two multiagent simulation models representing the existing sequential multiagent traffic simulations. The first model is fundamental diagram-based model, in which travelers do not interact directly and use a fundamental diagram of traffic flow to continuously compute their speeds. The second model is car-following based, in which travelers interact with their neighbors to adapt their speeds to their surrounding environment. Then we define patterns to distribute these simulations in a high-performance environment. The first is agent-based and distributes agents equally between available computation units. The second pattern is environment-based and partitions the environment over the different units. The results show that agent-based distribution is more efficient with fundamental diagram-based model simulations while environment-based distribution is more efficient with car following-based simulations.     

Intelligent agent-assisted adaptive order simulation system in the artificial stock market

Agent-based computational economics (ACE) has received increased attention and importance over recent years. Some researchers have attempted to develop an agent-based model of the stock market to investigate the behavior of investors and provide decision support for innovation of trading mechanisms. However, challenges remain regarding the design and implementation of such a model, due to the complexity of investors, financial information, policies, and so on. This paper will describe a novel architecture to model the stock market by utilizing stock agent, finance agent and investor agent. Each type of investor agent has a different investment strategy and learning method. A prototype system for supporting stock market simulation and evolution is also presented to demonstrate the practicality and feasibility of the proposed intelligent agent-based artificial stock market system architecture.     

High performance computing for three-dimensional agent-based molecular models

Agent-based simulations are increasingly popular in exploring and understanding cellular systems, but the natural complexity of these systems and the desire to grasp different modelling levels demand cost-effective simulation strategies and tools.In this context, the present paper introduces novel sequential and distributed approaches for the three-dimensional agent-based simulation of individual molecules in cellular events. These approaches are able to describe the dimensions and position of the molecules with high accuracy and thus, study the critical effect of spatial distribution on cellular events. Moreover, two of the approaches allow multi-thread high performance simulations, distributing the three-dimensional model in a platform independent and computationally efficient way.Evaluation addressed the reproduction of molecular scenarios and different scalability aspects of agent creation and agent interaction. The three approaches simulate common biophysical and biochemical laws faithfully. The distributed approaches show improved performance when dealing with large agent populations while the sequential approach is better suited for small to medium size agent populations.Overall, the main new contribution of the approaches is the ability to simulate three-dimensional agent-based models at the molecular level with reduced implementation effort and moderate-level computational capacity. Since these approaches have a generic design, they have the major potential of being used in any event-driven agent-based tool.     

Simulation Analysis Using Multi-Agent Systems for Social Norms

With the existence of the social customs or norms, Naylor demonstrates a possibility of stable long-run equilibria of support for a strike in a labor market, and this implies that at least some individuals will behave cooperatively and hence the prisoners’ dilemma could be escaped. In this paper, using an agent-based simulation model in which artificial adaptive agents have mechanisms of decision making and learning based on neural networks and genetic algorithms, we compare the results of our simulation analysis with that of the mathematical model by Naylor. In particular, while Naylor’s model is based on rationality as it relates to individual utility maximization, agents behave adaptively in our agent-based simulation model; agents make decisions by trial and error, and they learn from experiences to make better decisions.      

The Role of Network Topology and the Spatial Distribution and Structure of Knowledge in Regional Innovation Policy: A Calibrated Agent-Based Model Study

Recently, new regional innovation policy paradigms emerged that transcend a long-lived dispute on whether either regional specialization, diversification, or rather related variety is most conducive to regional innovativeness. This includes ‘smart specialization’ in which regions are deliberately specialized and connected following technological relatedness, ‘gatekeepers’ in which there are pipelines between dense regional networks, and a ‘regional mix’ of knowledge that allows sustained ‘branching’. We develop and use an agent-based model to study the conjecture that, to stimulate (supra)regional innovativeness, social planners need to consider both, in conjunction, the mix of technological knowledge in regions and the (inter)regional innovation network topology. We use this agent-based model to evaluate the performance of and study the internal mechanisms of these new policy paradigms in a variety of scenarios. To increase the external validity of our findings, we calibrate the knowledge graph searched by the agents in our model to the OECD patent database. We confirm that access to related variety is important, yet that, on top of that, access to incidentally related knowledge is crucial to prevent high-level lock-in and ensure long term technological progress. Moreover, we find that networks with regional gatekeepers are particularly innovative, because these gatekeepers form ‘knowledge hubs’, create short paths to potential partners, and enlarge the total pool of knowledge. In case agents have few relationships, we find exceptionally high performance for the gatekeeper network in combination with regional diversification. The smart specialization network is a solid second option, although it lacks access to incidentally related knowledge and thus will ultimately fall behind whenever agents have relatively few relationships. The study elaborates on specific scenarios to reveal intricacies in the relationship between knowledge distribution, network topology, and the structure of interrelationships between knowledge fields.     

无线传感器网络的移动Agent机制研究

针对无线传感器网络中传统C/S计算模式存在的资源浪费、负载不均衡等缺陷,提出了一种基于移动Agent的无线传感器网络模型,并对移动Agent进行了详细设计。实验结果表明,在无线传感器网络环境中,移动Agent模式在网络耗能和延时方面都具有一定的优势。     

Emerging small-world referral networks in evolutionary labormarkets

We model a labor market that includes referral networks using an agent-based simulation. Agents maximize their employment satisfaction by allocating resources to build friendship networks and to adjust search intensity. We use a local selection evolutionary algorithm, which maintains a diverse population of strategies, to study the adaptive graph topologies resulting from the model. The evolved networks display mixtures of regularity and randomness, as in small-world networks. A second characteristic emerges in our model as time progresses: the population loses efficiency due to over competition for job referral contacts in a way similar to social dilemmas such as the tragedy of the commons. Analysis reveals that the loss of global fitness is driven by an increase in individual robustness, which allows agents to live longer by surviving job losses. The behavior of our model suggests predictions for a number of policies     

Computing with the social fabric: The evolution of social intelligence within a cultural framework

Cultural algorithms employ a basic set of knowledge sources, each related to knowledge observed in various animal species. These knowledge sources are then combined to direct the decisions of the individual agents in solving optimization problems using an influence function inspired by the marginal value theorem from population biology. We briefly describe an implementation of this approach, the cultural algorithms toolkit (CAT) in the Repast agent-based simulation environment. Next we introduce the notion of "social fabric" which provides a framework in which the knowledge sources can access the social networks to which individuals can belong. A computational version of the social fabric idea is then implemented as an extension of the influence function in the CAT system. We then apply the enhanced system to a problem in engineering design, the "pressure vessel problem". For this problem, we show that the enhanced system with the social fabric outperforms the CAT system without it. We demonstrate also that the frequency with which the knowledge sources are able to access the network can affect the problem solving process.     

Comparing Strategies of Collaborative Networks for R&D: An Agent-Based Study

In this work we analyze the evolving dynamics of different strategies of collaborative networks that emerge from the creation and diffusion of knowledge. An evolutionary economic approach is adopted by introducing decision rules that are applied routinely and an agent-based model is developed. Firms (the agents) can collaborate and create networks for research and development purposes. We have compared three collaboration strategies (A—peer-to-peer complementariness, B—concentration process and C—virtual cooperation networks) that were defined on the basis of literature and on empirical evidence. Strategies are introduced exogenously in the simulation. The aims of this paper are twofold: (i) to analyze the importance of the networking effects; and (ii) to test the differences among collaboration strategies. It was possible to conclude that profit is associated with higher stock of knowledge and with smaller network diameter. In addition, concentration strategies are more profitable and more efficient in transmitting knowledge through the network. These processes reinforce the stock of knowledge and the profit of the firms located in the centers of the networks.     

Agent scheduling model for adaptive dynamic load balancing in agent-based distributed simulations

Agent-based distributed simulations are confronted with load imbalance problem, which significantly affects simulation performance. Dynamic load balancing can be effective in decreasing simulation execution time and improving simulation performance. The characteristics of multi-agent systems and time synchronization mechanisms make the traditional dynamic load balancing approaches not suitable for dynamic load balancing in agent-based distributed simulations. In this paper, an adaptive dynamic load balancing model in agent-based distributed simulations is proposed. Due to the complexity and huge time consuming for solving the model, a distributed approximate optimized scheduling algorithm with partial information (DAOSAPI) is proposed. It integrates the distributed mode, approximate optimization and agent set scheduling approach. Finally, experiments are conducted to verify the efficiency of the proposed algorithm and the simulation performance under dynamic agent scheduling. The experiments indicate that DAOSPI has the advantage of short execution time in large-scale agent scheduling, and the distributed simulation performance under this dynamic agent scheduling outperforms that under static random agent distribution.     

面向开源设计演化过程的产品-社区共生模型*

为探究开源设计的演化过程,本文首先对该过程的复杂性进行了分析;其次,建立了基于智能主体的产品-社区共生模型,该模型由多主体系统模型和产品关系模型组成,反映了人员、产品、社区的演化过程;最后,运用动态仿真技术对开源设计过程中的人员知识增长、产品进化周期、社区演化等方面进行了分析和评价。仿真实验结果证明了运用多主体建模与仿真方法对开源设计演化过程分析的有效性,为该过程的预测、管理和优化奠定了基础。     

传感器网络中基于移动代理的数据融合方法研究

在传感器网络中采用移动代理来进行数据融合是一个新颖的思路,它与传统的数据融合方法相比拥有诸多优势,有必要为数据融合设计一种基于移动代理的计算模型,使得移动代理在传感节点间迁移的同时能够进行有效的数据融合.我们对基于移动代理的数据融合方法进行了深入的探索,设计了基于移动代理的数据融合框架,提出了一种与移动代理路由紧密结合的按分辨率并行量化交叠的数据融合算法--PQOR,并将其成功地运用到目标分类识别的应用场景中.仿真结果表明:与传统的数据融合算法相比,PQOR能够以较小的代价达到应用的要求,其优势随着网络节点规模的增长更为明显.     

Mobile agents for information retrieval in hybrid simulation environment

In this paper, we propose a hybrid simulation environment that incorporates with wired/wireless networks, IEEE standard 1516 high-level architecture (HLA), and IBM Aglets mobile agent system. Therefore, HLA simulations are not restricted to be participated solely by using desktop computers with cable connections. Users can use a wide variety of devices to join in HLA simulations and explicitly exclude from junk data in terms of a personalized data filtering policy. Based on data correlation between HLA objects and a client's data filtering policy, we employ the simulation environment manager in distributing a client to an appropriate federate server (FS). In particular, a mobile agent, namely data filtering agent, is devised to temporarily reside at the FS to perform mobile agent-based data distribution management for clients. As a result, the clients can receive the most interested information corresponding to their pre-defined data filtering policies. Once either the data transmission quality within the wireless network is degraded below a threshold or the clients abnormally modify the data filtering policies, their own mobile agents carry out migrations to provide the users with the ubiquitous and seamless services. Consequently, the users can use any mobile device as well as using a desktop computer in a stationary point to participate in the HLA simulations. The experimental results also show that the proposed mobile agent-based data distribution can raise adaptability and applicability to large-scale HLA simulations.     

Agent-based load balancing on homogeneous minigrids: macroscopic modeling and characterization

In this paper, we present a macroscopic-characterization of agent-based load balancing in homogeneous minigrid environments. The agent-based load balancing is regarded as agent distribution from a macroscopic point of view. We study two quantities on minigrids: the number and size of teams where agents (tasks) queue. In macroscopic modeling, the load balancing mechanism is characterized using differential equations. We show that the load balancing we concern always converges to a steady state. Furthermore, we show that load balancing with different initial distributions converges to the same steady state gradually. Also, we prove that the steady state becomes an even distribution if and only if agents have complete knowledge about agent teams on minigrids. Utility gains and efficiency are introduced to measure the quality of load balancing. Through numerical simulations, we discuss the utility gains and efficiency of load balancing in different cases and give a series of analysis. In order to maximize the utility gain and the efficiency, we theoretically study the optimization of agents' strategies. Finally, in order to validate our proposed agent- based load balancing mechanism, we develop a computing platform, called simulation system for grid task distribution (SSGTD). Through experimentation, we note that our experimental results in general confirm our theoretical proofs and numerical simulation results from the proposed equation system. In addition, we find a very interesting phenomenon, that is, agent-based load balancing mechanism is topology-independent.     

An agent-based business automated system with self-adjusting visibility for reliability

This paper describes an agent-based model, called HAM, for business automation in large, distributed, and real-time systems. With resource bounds and time constraints, reliability and efficiency are difficult to achieve. We propose a self-restraining and self-stimulating control of agent interactions to meet the deadlines and to prevent agents from overloading system resources. The agent visibility and invisibility concepts are introduced and used to regulate the scopes of agent interactions and communications when it is needed. A mechanism, called DYVIREM, is designed to adjust agent visibility dynamically according to the deadlines and the resource limits. Through simulation experiments we also analyze the effects of agent visibility on the performance and on the quality of service of the proposed agent-based business model.     

Agent-based material handling and inventory planning in warehouse

Agent technology has become popular in the last few years as an effective approach to develop software and hardware systems. Our research applies this technology to warehouse systems. Since warehouse system is crucial to the success of supply-chain systems, objective of our research is to implement a framework of warehouse system based on the agent technology. This paper proposes an agent-based model for warehouse system, which is called AWAS (Agent-based model for WArehouse System). AWAS is composed of three subsystems, or agent-based communication system (ACS), agent-based material handling system (AMATH), and agent-based inventory planning and control system (AIPCON). These subsystems are designed to cooperate together to facilitate just-in-time exchange of orders and materials. Under these subsystems, seven kinds of basic agents are defined, including customer, supplier, order, inventory, product, supplier-order, and automatic-guided vehicle (AGV) agents. We will describe our approach in designing and implementing warehouse systems simulation with agents. Using a prototype system which was developed based on the model, the paper presents some results of simulation to show the validity of the model.     

Management decision support using long-term market simulation

In recent years Agent-based Computational Economics (ACE) has become an increasingly important method in market simulation. After liberalization of many former governmental owned or controlled industries the used operations research models are not longer sufficient to simulate market behavior due to individual action and increasing competition. Agent-based simulation appears to be an alternative approach considering also individual behavior and competition. Some short-term simulation approaches have shown promising results for the simulation in the domain of electricity markets. Picking up the desire for a long-term oriented simulation, this paper presents a basic agent-based model considering the investment decision within long-term planning of electricity markets. Additionally, regulatory agents are introduced as a third side in the market simulation to represent governmental decisions. This results in the definition of three types of agents representing electricity generating companies, consumers and governmental instances.     

基于Agent的复杂系统分布仿真建模方法的研究

基于Agent的分布仿真是研究大型复杂系统的一种有效的、重要的方法。为了减小复杂系统仿真的复杂度,增加仿真模型的重用和可维护性,需要研究基于Agent分布仿真的建模方法。首先对复杂系统及其特性进行了分析,对基于Agent的仿真进行了全面的论述,然后对基于Agent的复杂系统仿真中的复杂系统建模分析、Agent建模分析以及Agent的分布进行了分析,给出了基于Agent的复杂系统分布仿真的建模步骤,最后给出了在此建模思想指导下的金融证券市场的建模过程。     

基于移动代理的卫星网路由性能研究

通过仿真方法分析代理系统中影响基于移动代理卫星网路由算法性能的关键因素,给出了拟仿真的路由算法及其NS2仿真方案,详细阐述了使用Grasshopper、JADE和Linux、运行于局域网环境、支持代理的卫星网仿真平台构建方案,包括仿真平台的系统结构、卫星节点功能实体、路由代理交互接口、星际链路仿真模块等。通过与NS2中的仿真结果对比,得出了高链路时延下的移动代理迁移效率是影响路由算法性能的关键因素,并通过代理系统间横向比较,给出了路由算法开发以及仿真平台构建过程中代理系统的选择建议。