A land-use spatial optimum allocation model coupling a multi-agent system with the shuffled frog leaping algorithm

The optimum allocation of land resources is an important process that promotes the intensive use of land resources and facilitates sustainable development. The main land-use optimum allocation models generally tend to ignore the influence of human activities on the optimization of land-use; thus, leading to inaccurate optimization results for the land-use spatial layout and impeding sustainable land-use planning. Some of these models consider human activities, but lack global optimization goals and iterative search mechanisms, and therefore have no optimization function and provide unsatisfactory optimization results. Therefore, this study coupled the multi-agent system (MAS) that contains land-use planning knowledge with the search iteration mechanism in the shuffled frog leaping algorithm (SFLA) and rebuilt the local optimization behavior of the SFLA; thus, developing a new kind of land-use optimum allocation model, the so-called “multi-agent shuffled frog leaping algorithm” (MASFLA). The model was then applied to the Jizhou District of Tianjin City, and there were three key simulation results. (1) The MASFLA had remarkable optimization effects on land resource use, and the optimized values of ecosystem services, regional economic output, and land-use intensive degree increased by 18.6, 51.1, and 30.3%, respectively. (2) The MASFLA had better optimization effects than a single algorithm, and its comprehensive fitness value was 2.1 and 4.1% higher than that of the SFLA and the particle swarm optimization (PSO), respectively, under the same convergence conditions. (3) The MASFLA model could relieve the land-use conflicts between different decision-making agents and realize the optimum allocation of regional land-use in terms of both spatial structure and quantity under multiple optimization goals and restrictions; thus, increasing the local economic output, improving the land-use spatial compactness and ecological environment, and promoting the sustainable use of regional land.     

A state-based multi-agent system model of taxi fleets

Multimedia Tools and Applications - Management and control of transportation systems benefit from simulation modeling. The design of the corresponding models is difficult because of their...     

生态化MAS的认知模型

生态化MAS是由异构的Agent所组成的一种具有协同进化的多Agent系统。提出了生态化MAS的理念和认知模型,它是形成Agent知识和信念的基本框架;设计了一个模型检验的程序结构,并通过典型应用表明,该模型能够使Agent对环境的认知达到三个层次——概念图、模糊词计算和关联性框架。该模型能够为基于Agent的问题求解提供依据。     

基于多Agent的可重构制造系统集成模型

应用控制、管理和维护一体化的自动化技术,建立了基于多Agent的可重构制造系统RMS（Reconfigurable Manufacturing System）集成模型。该模型集成了基于多Agent 的RMS重构模型、控制模型和故障诊断模型,将RMS的控制、管理和维护联系起来,并给出了该模型的UML（Unified Modeling Langurage）活动图,最后举例验证了模型的可行性。     

信息生态系统的多主体(Agent)交互模型

从生态学原理出发,提倡信息生态,将信息社会纳入可持续发展的轨道意义重大。本文将企业、地区等信息实体抽象为信息主体,定义了反映信息主体在不同领域内的知识水平指数,以及信息交流和自学创新对指数的影响,对信息生态系统建立了基于多主体(Agent)的信息交互模型,“自底向上”对主体信息行为、系统信息多样性、系统信息分化、自学创新的作用等问题进行模拟研究。研究结果表明,在信息社会中,若仅仅依靠信息交流,随着时间的演进,信息的差异性下降;系统知识水平的增长率减缓,最终将趋于停滞。结果还显示信息优势群体和弱势群体在信息是交流还是共享的不同情况下,其相对信息优势基本不变,弱势群体要改变上述情况,必需知识更新和提高自身的知识创新能力。本研究为建立系统化的信息生态科学体系做了铺垫。     

Balanced reactive-deliberative architecture for multi-agent system for simulation league of RoboCup

This paper presents an architecture for a multi-agent system for the RoboCup simulation league. It consists of a dynamic dual behavior-based architecture for an intelligent agent, a behavior-based decision algorithm, and a dynamic role-based multi-agent cooperation model. A new concept called confidence function is introduced to balance reactivity and deliberation. This architecture was implemented in a team, and match results demonstrate its validity.     

多代理仿真中自适应模型融合研究

多代理仿真是计算机仿真迈向智能化的核心技术,仿真模型的适配是确保仿真有效性、科学性以及实时性的基础,如何确保在仿真过程中数据的解算与仿真模型的无缝融合,是解决各类仿真环境与仿真应用系统集成的关键问题之一。提出了一种自适应的模型融合机制,利用多代理自身的灵活特性,采用基于关联度的模型关联机制,为分布式仿真研究提供智能化、自动化的模型适配手段。通过与某仿真引擎自有的模型管理机制进行性能对比,实验结果表明基于关联度的模型调度机制优于分布式仿真引擎的模型管理策略。     

A general computational recognition primed decision model with multi-agent rescue simulation benchmark

Analytical decision making strategies rely on weighing pros and cons of multiple options in an unbounded rationality manner. Contrary to these strategies, recognition primed decision (RPD) model which is a primary naturalistic decision making (NDM) approach assumes that experienced and professional decision makers when encounter problems in real operating conditions are able to use their previous experiences and trainings in order to diagnose the problem, recall the appropriate solution, evaluate it mentally, and implement it to handle the problem in a satisficing manner. In this paper, a computational form of RPD, now called C-RPD, is presented. Unified Modeling Language was used as a modeling language to represent the proposed C-RPD model in order to make the implementation easy and obvious. To execute the model, RoboCup Rescue agent simulation environment, which is one of the best and the most famous complex and multi-agent large-scale environments, was selected. The environment simulates the incidence of fire and earthquakes in urban areas where it is the duty of the police forces, firefighters and ambulance teams to control the crisis. Firefighters of SOS team are first modeled and implemented by utilizing C-RPD and then the system is trained using an expert’s experience. There are two evaluations. To find out the convergence of different versions developed during experience adding, some of the developed versions are chosen and evaluated on seven maps. Results show performance improvements. The SOS team ranked first in an official world championship and three official open tournaments.     

A privacy-preserving model for multi-agent propositional planning

Abstract

Over the last years, the planning community has formalised several models and approaches to multi-agent (MA) propositional planning. One of the main motivations in MA planning is that some or all agents have private knowledge that cannot be communicated to other agents during the planning process and the plan execution. We argue that the existing models of the multi-agent planning task do not maintain the agents’ privacy when a (strict) subset of the involved agents share confidential knowledge, or when the identity/existence of at least one agent is confidential. In this paper, first we propose a model of the MA-planning tasks that preserves the privacy of the involved agents when this happens. Then we investigate an algorithm based on best first search for our model that uses some new heuristics providing a trade-off between accuracy and agents’ privacy. Finally, an experimental study compares the effectiveness of using the proposed heuristics.     

A Markovian and cellular automata land-use change predictive model of the Usangu Catchment

Usangu Catchment, in Tanzania, is vital for its rice production in which more than 30% of Tanzanian rice is grown. The catchment is a part of the Southern Agricultural Corridor of Tanzania where major agricultural intensification is expected to take place. Given the role of this catchment, it is important to investigate the effect of agricultural intensification, land-use/land-cover (LULC) change and climate variability on water balance in the catchment. Thus, the objective of the study was to simulate Usangu Catchment’s LULC of 2020 based on LULC of 2000, 2006 and 2013 using Markov Chain and Cellular Automata Analysis.Social, edaphic, climatic and landscape geomorphology factors governing the LULC change and distribution were used to prepare LULC suitability maps in geographical information system.The relative importance of LULC change factors was determined using the analytic hierarchy process and aggregated using weighted linear combination under multi-criteria evaluation approach. The model was validated using simulated and observed LULC 2013. The standard kappa coefficient (κ-standard) and overall agreements of the model were 0.6776 and 0.9125, respectively. The error due to quantity is 0.0243 while error due to allocation is 0.0667. The simulated LULC 2020 scenario shows the increase in urban area by 8.2% and a major decrease in forestland and shrubs by 20.6% and 6.9%, respectively. About 19.6% grassland and 8.5% of agricultural land in 2013 will be converted to urban land by 2020. On the other hand, about 372.0 km² (10.4%) of wetlands and 368.2 km² (10.3%) of woodlands will be converted to agricultural land. The 2020 LULC simulation model of Usangu developed in this study provide some useful information for future LULC scenarios and data for water balance models and preparation of future ecological conservation plans.     

基于Multi-Agent的复杂网络疾病传播仿真模型

通过复杂网络上的多智能体的建模与仿真,对疾病的传播过程进行了模拟,研究了疾病传播在不同条件下的传播特征。通过设定采取4个仿真过程,得出疾病的传播即使是以极小的概率治愈也能使系统得到良性发展,验证了前期疫苗式免疫的重要性等结论。     

A simnet simulation model for estimating system reliability

A general SIMNET simulation model is developed for estimating system reliability. The input data to the model is comprised of the minimal cut sets of the block diagram representing the system. The time-to-failure of the (parallel-series) components may be descriebd by different distributions. The model can be readily extended to include repair and maintenance of the components.     

一种适合EAI的多Agent系统模型

企业应用集成是当前研究的热点，应用集成是能够满足企业统一信息管理、统一资源调配和优化业务流程的重要手段。目前应用集成尚无公认的标准，在实施集成中主要考虑标准接口问题，而往往忽略本身体系结构的合理性。要满足企业为适应市场竞争而灵活变动的管理模式，应用集成必须具备较高的可扩展性和可灵活调整性。在分析企业应用集成内涵的基础上，提出了适合于企业应用集成特点的多Agent系统模型。     

A Q-learning-based multi-agent system for data classification

In this paper, a multi-agent classifier system with Q-learning is proposed for tackling data classification problems. A trust measurement using a combination of Q-learning and Bayesian formalism is formulated. Specifically, a number of learning agents comprising hybrid neural networks with Q-learning, which we have formulated in our previous work, are devised to form the proposed Q-learning Multi-Agent Classifier System (QMACS). The time complexity of QMACS is analyzed using the big O-notation method. In addition, a number of benchmark problems are employed to evaluate the effectiveness of QMACS, which include small and large data sets with and without noise. To analyze the QMACS performance statistically, the bootstrap method with 95% confidence interval is used. The results from QMACS are compared with those from its constituents and other models reported in the literature. The outcome indicates the effectiveness of QMACS in combining the predictions from its learning agents to improve the overall classification performance.     

一种适用于Internet的实时多Agent系统模型

提出了一种基于Agent to Agent架构的,适用于Internet环境的实时多Agent系统模型。重点阐述了该模型中Agent之间的协作方式、调度策略和任务执行的控制策略,并介绍了采用的实时Agent通信语言和通信机制。     

基于政策的层次系统协作模型

为了解决大型层次系统中多Agent间的协作问题,提出了一种基于政策的协作模型。首先分析层次系统中Agent间协作的特点,然后给出相关概念定义,构建Agent政策框架及政策分享机制,最后提出协作模型算法。实践表明,该模型能够在大型、分布式环境中实现资源和服务的协同,使Agent的行为决策更加规范可控,因此具有高度的灵活性及扩展性,对于解决层次MAS协作问题具有一定的参考价值。     

A multi-agent based approach for change management in manufacturing enterprises

Change management becomes an unavoidable necessity for manufacturing enterprises. Since change in business processes carries significant impact on the performance of manufacturing companies, a change management model is definitely required to remain competitive. Moreover, utilizing agent based systems will provide computational provision and integrity to manage and measure the capabilities to follow the change in a progressive approach by employing the cooperation and collaboration properties of various agents helping for retrieval of the required information in a rapid way. Therefore, in this paper, a multi-agent based change management model is proposed to handle the changes in manufacturing enterprises. The model is validated through a case study done to measure the performance of change management capabilities in a manufacturing company. A sensitivity analysis on the results of this case study is also conducted to reveal the system reactivity to various parameters.     

A multi-agent system model of a diesel engine using rule induction

The proliferation of a multi-agent system (MAS) and ideas from Artificial Intelligence (AI)/distributed AI have changed the way systems, in general are controlled, and operation of a system (diesel engine) in particular is automated. In this paper a distributed multi-agent architecture for a diesel engine and the knowledge sources that handle electricity generation is developed. Electronic devices and components used for data handling are described. The sensed data are presented in fuzzy logic and calculated in entropy values and depicted in a decision hierarchy. A comparative performance assessment of the proposed multi-agent based system with an existing system is presented and discussed.     

一种基于多Agent的双向智能自动匹配系统模型

研究了信息在计算机网络中的双向智能匹配问题;给出了一种基于多Agent技术的双向智能自动匹配系统模型,阐述了其组成架构、工作原理及流程,提出了多Agent间协作机制和信息的模糊匹配;实现了信息搜索过程中信息高速精准的双向智能自动匹配,并指出了它并行性的发展方向。