基于多Agent的复合模型求解自适应QoS机制

在基于网络的分布式系统应用基础上，分析了大型复杂问题复合模型协作求解的过程特征描述，提出基于多Agent 的领域问题协作求解的主动控制策略，探讨了用户交互Agent、系统主控Agent、协作Agent以及模型Agent和数据Agent等复合模型协作求解的4种Agent类型。应用多Agent层次结构，提出一种复合模型协作求解的自适应QoS体系结构，通过实现复合模型协作求解的主动调度规划算法对其进行了验证，支持分布式网络环境下实现模型资源和数据资源的共享，以提高协同计算环境分布式问题协作求解的运行效率和服务质量。     

Agent协作的拼配规划系统ABPS的研究与实现

原料拼配方案确定是一个复杂的经验类问题求解，不仅涉及原料的种类和品质、拼配 后的品质体系，而且要考虑陈（老）原料使用、合理库存量、成本等多因素约束．本文在分 析和获取拼配知识的基础上，建立了一种基于Agent协作的拼配规划系统ABPS，在知识表达 和拼配模型建立的基础上，通过创建控制Agent(CA)和执行Agent(EA)来实现知识与模型结合 、计算与推理并行、协作交互控制的分布式处理．     

一种引入局部交互的群体协作行为协同进化机制

现有协同进化模型在求解子系统间存在相互关联作用的问题时存在不足，使其难以高效产生群体复杂适应性协作行为．针对这一问题，依据系统论和非线性科学理论，构造了一种引入局部交互的群体复杂协作行为协同进化机制．该机制通过局部交互作用探测局部启发信息，并结合全局启发信息共同引导协同进化过程，从而使求解过程朝着正确的方向进化．算法分析及多机器人协作推箱实验表明，该机制模型及其算法有效地克服了现有协同进化模型的不足和局限，能使复杂关联的群体协作行为高效地朝着全局最优协作方向演化．     

面向对象工作流管理系统模型设计

工作流管理系统实现的难点在于对动态重构和复杂协作的支持．本文从对象的提取与定义,工作流协作机制的实现及基于分布对象的动态重构提出了一种能解决上述问题的工作流管理系统的模型设计．     

订单可分的协作计划模型及其进化算法

在传统协作规划中协作任务作为不可分订单在协作伙伴间进行协商协作,其协作模型和优化求解算法也是基于任务订单不可分的.为提高供应链的资源利用效率,进一步降低协作成本,建立订单可拆分的协作计划模型;设计了一种离散优化与连续优化相结合的差分进化算法,该算法具有优化能力强、陷入局部最优的概率小等特点,在实现了任务订单拆分后的任务协作的同时,扩大了供应链的协作优化的空间,使协作决策更加准确有效;最后,通过相应算例验证了模型和算法的有效性.     

基于协作的协同设计过程控制对象知识建模技术

在分析协同设计领域问题的协作求解模式基础上，分析了协同设计领域问题求解的过程控制对象，提出了协同设计领域问题求解过程控制对象的多层次协作求解机制。并以领域问题协作求解、业务处理的过程控制以及领域控制知识的处理过程为核心，构建基干协作的协同设计过程控制对象的知识处理模型。最后以活动框架的业务处理过程控制为背景，讨论了协同设计领域问题求解过程控制对象的知识处理算法及其程序实现过程。     

PMBC及其在Robocup中的应用

智能体间的协作能够提高多智能体系统的智能度。而规划作为一种重要的问题求解技术，能够有效地实现多智能体间的协作。该文介绍了一种基于协怍的规划模型及此模型的前提、动作和终止条件三要素，通过对特定状态和局部协作的提前规划，有效地实现了多智能体系统中智能体间的协作。通过把此规划模型运用到典型的多智能体系统一机器人足球比赛中，证明了在多智能体系统中应用此规划模型不仅能够提高单个智能体的反应速度，还可以提高整个系统的运行效率。     

CSCW的一种建模与实现方法

协作模型是CSCW系统的核心和基础.其目的是描述群体协作的方式、机制、管理、协调以及对协作过程的控制等.根据群体协同工作的方式、特点和需求,提出了基于交互、活动和协作三层结构的协作模型,并给出了交互、活动和协作的形式化定义,最后提出了一种采用"镜头焦点"和"自由交互"相结合的协作模型实现方法.本文提出的协作模型及其实现方法在分布式多媒体协同工作系统DMCS中得到实现.     

多机器人不确定协作任务的动态优化方法

针对一类可变目标的多机器人协作运动问题，提出动态优化的方案．在每一优化时刻 根据当前目标状态及其变化规律，确定每个机器人的运动，得到该时刻概率意义上的最优运 动．把系统整体路径规划的复杂问题分解为独立路径规划问题和分派问题分别求解，实现最 快协作运动．并且将这种优化方案动态实施，以适应目标的不确定性．提出的方法还可推广 到更一般的不确定协作任务中．     

基于BDI模型的多机器人智能体系统设计

BDI模型是智能体设计的一种成熟结构，本文将BDI模型应用于多机器人智能体系统设计中．文章先从形式逻辑角度描述系统模型，然后讨论基于合同网的多机器人智能体的协作机制，最后给出基于BDI模型的多机器智能体的实现模型．     

并发成员系统程序设计语言环境下的机器人协同问题求解

本文首先提出并证明了模型世界下双机器人协同问题求解的难解性，然后介绍了已开发成功的支持多种特别是协同问题求解的并发成员系统程序设计语言ＭＳ－１在该问题领域的成功应用。     

Bipartite tracking of homogeneous and heterogeneous linear multi-agent systems

In this paper, we consider bipartite tracking of linear multi-agent systems with a leader. Both homogeneous and heterogeneous systems are investigated. The communication between agents is modelled by a directed signed graph, where the negative (positive) edges represent the antagonistic (cooperative) interactions among agents. Linear Quadratic Regulator (LQR)-based approach is used to derive the distributed protocol for the follower agent to achieve bipartite tracking of the leader. It is shown that solving the bipartite tracking problem over the structurally balanced signed graph is equivalent to solving the cooperative tracking problem over a corresponding graph with nonnegative edge weights. This bridges the gap between the newly raised bipartite tracking problem and the well-studied cooperative tracking problem. Three novel control protocols are proposed for both cooperative and bipartite output tracking of heterogeneous linear multi-agent systems. Numerical examples are given to show the effectiveness of our control protocols.     

Explanations from knowledge-based systems and cooperative problem solving: an empirical study

Explanations are expected to play an important role when knowledge-based systems are used for cooperative problem solving. In this context both human and machine contribute to the procedures, constraints and strategies used in the problem-solving process. An increased need for explanations in this context is congruent with a cognitive-effort perspective and the Production Paradox observed with on-line help systems. This article describes an investigation of the role of explanations in cooperative problem solving. An experimental field study was performed with 41 users and an operational system for personal financial planning. The experiment showed a requirement for cooperative problem solving was associated with greater use of explanations. This effect was more marked with those users who had more fully explored the use of explanations during a preliminary stage of guided problem solving. The frequency of use of explanations in total was positively related to problem-solving performance. There was some evidence that the positive relationship between explanations and improved performance was more noticeable when problems requiring cooperation were undertaken.     

Solving the traveling salesman problem using cooperative genetic ant systems

The travelling salesman problem (TSP) is a classic problem of combinatorial optimization and has applications in planning, scheduling, and searching in many scientific and engineering fields. Ant colony optimization (ACO) has been successfully used to solve TSPs and many associated applications in the last two decades. However, ACO has problem in regularly reaching the global optimal solutions for TSPs due to enormity of the search space and numerous local optima within the space. In this paper, we propose a new hybrid algorithm, cooperative genetic ant system (CGAS) to deal with this problem. Unlike other previous studies that regarded GA as a sequential part of the whole searching process and only used the result from GA as the input to subsequent ACO iterations, this new approach combines both GA and ACO together in a cooperative manner to improve the performance of ACO for solving TSPs. The mutual information exchange between ACO and GA in the end of the current iteration ensures the selection of the best solutions for next iteration. This cooperative approach creates a better chance in reaching the global optimal solution because independent running of GA maintains a high level of diversity in next generation of solutions. Compared with results from other GA/ACO algorithms, our simulation shows that CGAS has superior performance over other GA and ACO algorithms for solving TSPs in terms of capability and consistency of achieving the global optimal solution, and quality of average optimal solutions, particularly for small TSPs.     

面向产品协同设计的分布式决策支持系统研究

在传统决策支持系统基础之上,本文提出了面向协同设计的分布式决策支持系统的体系结构,分析了问题处理系统与分布式问题求解的过程,并讨论了分布式决策支持系统的、基于智能Agent集成控制的多库系统及其管理。     

成员系统模型及其归约

本文提出了一种基于有限关联团块观点的成员系统模型，主要针对不确定性情况给出了其归约操作的形式化描述，讨论了它与产生式系统的关系，并根据该模型及其归约过程，成功地设计实现了一种并发成员系统程序计算语言，对研究支持多种ＡＩ问题求解的模型及其语言做了有益的尝试。     

Decentralised Formation Control and Stability Analysis for Multi-vehicle Cooperative Manoeuvre

Multi-vehicle cooperative formation control problem is an important and typical topic of research on multi-agent system. This paper presents a formation stability conjecture to conceive a new methodology for solving the decentralised multivehicle formation control problem. It employs the "extensiondecomposition-aggregation" scheme to transform the complex multi-agent control problem into a group of sub-problems which is able to be solved conveniently. Based on this methodology, it is proved that if all the individual augmented subsystems can be stabilised by using any approach, the overall formation system is not only asymptotically but also exponentially stable in the sense of Lyapunov within a neighbourhood of the desired formation. Simulation study on 6-DOF aerial vehicles (Aerosonde UAVs) has been performed to verify the achieved formation stability result. The proposed multi-vehicle formation control strategy can be conveniently extended to other cooperative control problems of multi-agent systems.      

Socio-Cognitive Factors in the Acquisition and Transfer of Knowledge

Within cooperative learning great emphasis is placed on the benefits of ?two heads being greater than one?. However, further examination of this adage reveals that the value of learning groups can often be overstated and taken for granted for different types of problems. When groups are required to solve ill-defined and complex problems under real world constraints, different socio-cognitive factors (e.g., metacognition, collective induction, and perceptual experience) are expected to determine the extent to which cooperative learning is successful. Another facet of cooperative learning, the extent to which groups enhance the use of knowledge from one situation to another, is frequently ignored in determining the value of cooperative learning. This paper examines the role and functions of cooperative learning groups in contrast to individual learning conditions, for both an acquisition and transfer task. Results for acquisition show groups perform better overall than individuals by solving more elements of the Jasper problem as measured by their overall score in problem space analysis. For transfer, individuals do better overall than groups in the overall amount of problem elements transferred from Jasper. This paradox is explained by closer examination of the data analysis. Groups spend more time engaged with each other in metacognitive activities (during acquisition) whereas individuals spend more time using the computer to explore details of the perceptually based Jasper macrocontext. Hence, results show that individuals increase their perceptual learning during acquisition whereas groups enhance their metacognitive strategies. These investments show different pay-offs for the transfer problem. Individuals transfer more overall problem elements (as they explored the context more) but problem solvers who had the benefit of metacognition in a learning group did better at solving the most complex elements of the transfer problem. Results also show that collective induction groups (ones that freely share) – in comparison to groups composed of dominant members – enhance certain kinds of transfer problem solving (e.g., generating subgoals). The results are portrayed as the active interplay of socio-cognitive elements that impact the outcomes (and therein success) of cooperative learning.