基于仿人智能控制的角色分配模型在机器人足球系统中的运用

角色分配是机器人动作协调过程中必须解决的问题之一.角色分配的数学本质就是一种二部图的匹配问题,仿人智能控制器控制决策的二次映射结构提供了一种具有普适意义的控制结构以解决这类控制问题.以此为基础,提出了由决策分配策略选择器、角色分配策略库、角色冲突协调器以及时序系统构成的基于仿人智能控制的角色分配器结构模型作为角色分配的参考模型,总结了这种角色分配器的设计方法.最后介绍了这种模型在足球机器人系统中的运用实现.在实际足球机器人系统(MiroSot系统)中的成功运用证明了该模型的有效性.     

足球机器人动态角色分配策略仿真研究

研究足球机器人角色分配问题,角色分配是足球机器人决策协调的关键,在比赛中要兼顾最优性与实时性进行角色分配是足球机器人技术的难点.针对足球机器人比赛的特点,提出一种基于市场机制的动态角色分配算法.为了使角色分配接近最优,综合考虑影响角色分配的距离、角度以及其它多种不确定性因素来设计竞标函数;为了保证实时性,系统根据赛场态势,采用分级竞标的策略动态调整机器人角色.实验表明,市场动态算法在最小时间内提供接近最优化的角色分配方案,兼顾了足球机器人角色分配的实时性与最优性.     

舰艇编队信息战火力分配微分对策模型及求解

火力分配问题一直是军事理论工作者和参战双方指挥员关心得最多的重要问题之一. 针对舰艇编队信息战的特殊性, 本文利用兰彻斯特战斗方程和微分对策理论建立了相应的火力分配优化模型, 并进行了模型分析和求解. 通过分析、求解一个实例的最优策略, 从另一层面证明了“集中优势火力”这一战术思想的正确性. 研究结果可为作战指挥决策提供理论参考.     

火力分配问题研究

火力作战是现代化作战的基本形式,是达成作战目的的重要作战手段。可靠的火力打击是部队完成作战任务的关键环节,而正确的火力打击行动来源于正确的火力打击决策。分析火力分配对于指挥员作出正确的火力打击决策具有重要的指导意义。本文首先对火力分配的概念进行阐述,其次对火力分配问题的相关模型和求解算法进行分析和总结。     

基于路径开销的机器人足球角色分配设计

小型足球机器人的决策系统是一个多智能体协调控制系统,主要由视觉信息处理、协调策略、角色分配、动作实现等组成.本文对角色分配机制进行研究,提出了一种路径开销组合最优的角色分配算法,实现了Play策略下动态角色分配的整体设计.仿真平台测试表明了机器人的整体配合能力有较大提高.     

基于Play策略的小型足球机器人角色分配设计

小型组足球机器人的决策系统是一个多智能体的协调控制系统，主要由视觉信息处理、协调策略、角色分配、动作实现等组成。文中对角色分配进行研究，提出了一种基于Hay策略并与区域相关的角色分配方案，并阐述了决策系统下角色分配的整体设计。实现了动态灵活的分配机制。     

要地防空协同射击打击效能辅助决策研究

在网络中心化的背景下,对弹炮混编防空群对目标的探测能力、可射击能力和毁伤能力进行了分析,研究了防空群的整体打击效能,重点分析了协同射击模式下的火力分配方案.通过实例研究可以看出,分析防空群的整体打击效能,可以选出最佳的火力分配方案,为指挥员配置防空武器起到了辅助决策的作用.     

静态博弈模型下的无线传感器网络MAC地址分配算法

针对传感器网络地址分配过程中为了避免地址冲突而导致通信能耗较大的问题,提出了一种基于静态博弈模型的MAC地址分配算法MAAS.该算法将传感器节点看作博弈模型中的决策者,通过使模型达到纳什平衡来解决节点的MAC地址分配问题.在进行地址分配时,MAAS利用博弈模型中每个决策者可根据其邻居节点信息独立进行决策的性质,避免了节点在地址分配过程中发送大量的交互信息.实验结果表明,MAAS在保证较低冲突率的同时降低了地址分配过程中的通信消耗.     

基于遗传算法的防空兵群(团)火力分配模型

防空兵火力分配决策是防空兵群团射击指挥的重要内容,火力分配的正确性和实时性关系到防空作战的成败,是防空兵指挥自动化系统追求的目标.在解决防空兵火力分配问题时,以往常采用整数规划的方法.但在实际应用中,传统方法存在着三个方面的不足.为此,该文将标准遗传算法运用于防空兵火力分配方案寻优中,建立了新的基于遗传算法的防空兵群团火力分配模型和计算流程,并给出了计算示例.计算结果表明,该方法较好地克服了传统方法的缺陷,具有良好的实用价值和应用前景.     

足球机器人系统中角色分配策略的设计

介绍了足球机器人系统以及决策子系统结构,该结构分为数据信息预处理,角色分配,动作安排.轮速输出。详细说明了基于机器人足球比赛的多智能体协作角色分配问题,提出了一种优化综合指标进行角色分配的方法,并在实际系统中得到验证。     

The feasibility of axiomatically-based expert systems

We distinguish axiomatically-based expert systems, whose design and implementation are guided by one or more axiomatically-based theories of decision-making (e.g., decision theory, Bayesian probability theory, maximum entropy theory), from traditional expert systems. An analysis of the knowledge acquisition and computational needs of axiomatically-based expert systems is presented. An explicit quantitative comparison is made between the actual knowledge acquisition effort required to build an existing expert system, and the effort that would be required to build an analogous axiomatically-based advice system. The costs and benefits of the axiomatic approach are discussed. The analysis suggests that the small additional cost of knowledge acquisition for the axiomatic approach are outweighed by the long-term benefits this approach provides.     

Decision making on the sole basis of statistical likelihood

This paper presents a new axiomatic decision theory for choice under uncertainty. Unlike Bayesian decision theory where uncertainty is represented by a probability function, in our theory, uncertainty is given in the form of a likelihood function extracted from statistical evidence. The likelihood principle in statistics stipulates that likelihood functions encode all relevant information obtainable from experimental data. In particular, we do not assume any knowledge of prior probabilities. Consequently, a Bayesian conversion of likelihoods to posterior probabilities is not possible in our setting. We make an assumption that defines the likelihood of a set of hypotheses as the maximum likelihood over the elements of the set. We justify an axiomatic system similar to that used by von Neumann and Morgenstern for choice under risk. Our main result is a representation theorem using the new concept of binary utility. We also discuss how ambiguity attitudes are handled. Applied to the statistical inference problem, our theory suggests a novel solution. The results in this paper could be useful for probabilistic model selection.     

文化算法的收敛性分析

虽然文化算法已被广泛应用于解决各个领域的优化问题,但与其收敛能力相关的理论分析还比较缺乏。为此,针对传统文化算法,应用有限状态Markov链来分析文化算法的搜索过程,进一步使用公理化模型深入研究了种群在决策空间上的概率分布情况,证明了在文化算法信度空间的标准知识、拓扑知识和状况知识引导下,变异算子和最优保留选择策略保证了文化算法依概率弱收敛到全局最优解。     

An invitation to economical test spaces and effect algebras

We introduce economical tests of an economical system which lead to effect algebras, structures which are more general than Boolean algebras or λ-systems, which were recently used to describe unambiguous events, to model situations for example in decision theory when the Kolmogorov axiomatic is not applicable.     

An Axiomatic Foundation for Yager's Decision Theory

Yager's decision theory, based on the ordered weighted averaging operators and Dempster–Shafer theory, is a very important theory for modeling decision making under uncertainty. This paper attempts to provide an axiomatic foundation for this theory, and thus offers a reasonable interpretation of its basic concept. The properties, presented in terms of observable preferences, enable us to elicit the subjective degree of optimism and the mass function over the set of states. Moreover, some important properties of this decision theory have been established, which in turn show that no Dutch Book argument can be made against agents in this theory. This paper also illustrates the idea of the central result by an example with a particular kind of operators.     

模糊近似空间上的粗糙模糊集的公理系统

粗糙集理论是近年来发展起来的一种有效的处理不精确、不确定、含糊信息的理论，在机器学习及数据挖掘等领域获得了成功的应用．粗糙集的公理系统是粗糙集理论与应用的基础．粗糙模糊集是粗糙集理论的自然的有意义的推广．作者研究了模糊近似空间上的粗糙模糊集的公理系统，用三条简洁的相互独立的公理完全刻划了模糊近似空间上的粗糙模糊集，同时还把作者给出的公理系统与粗糙集的公理系统做了对比，指出了两者的区别．     

Characterization of the linear weighted sum decision principle

Recently the multicriterion decision problem (MCDP) has attracted more and more attention, and many decision principles have been proposed for it, among which the linear weighted sum decision principle is the most widely used. This paper discusses an axiomatic characterization of that principle to find out its essential meaning. Firstly, we give an axiomatic system characterizing it. Secondly, we examine the meanings of the axioms. Thirdly, we try to give a logical explanation of why the linear weighted sum decision principle is so popular by comparing it with other decision principles for the MCDP and for decision making under uncertainty. Finally, we investigate whether or not decision principles for the MCDP are applicable to decision making under uncertainty and vice versa.     

Approaches to Pythagorean Fuzzy Stochastic Multi‐criteria Decision Making Based on Prospect Theory and Regret Theory with New Distance Measure and Score Function

In this paper, we initiate a new axiomatic definition of Pythagorean fuzzy distance measure, which is expressed by Pythagorean fuzzy number that will reduce the information loss and remain more original information. Then, the objective weights of various criteria are determined via grey system theory. Combining objective weights with subjective weights, we present the combined weights, which can reflect both the subjective considerations of the decision maker and the objective information. Meanwhile, a novel score function is proposed. Later, we present two algorithms to solve stochastic multicriteria decision making problem, which takes prospect preference and regret aversion of decision makers into consideration in the decision process. Finally, the effectiveness and feasibility of approach is demonstrated by a numerical example.     

分布式多传感器决策融合的新方法——双端检验法

利用传统的Ｂａｙｅｓｉａｎ决策理论和假设检验方法处理分布决策融合问题有一个重要的缺陷就是缺乏柔性，无法将不确定与不知道区分开来；Ｄｅｍｐｓｔｅｒ－Ｓｈａｆｅｒ证据理论虽然可以弥补这一缺陷，但作为其数学基础的公理化定义的严密性值得怀疑。因此需要有更完善、更可靠和更有效的统计决策及证据组合方法。在这方面Ｔｈｏｍｏｐｏｕｌｏｓ已提出了一种广义证据处理方法（ＧＥＰ），它由Ｂａｙｅｓｉａｎ理论与Ｄ－Ｓ理论相     

FEELING DECISION SYSTEMS AND QUANTUM MECHANICS

From the viewpoint that the decision based on feeling has a structure similar to that of a quantum measurement process, we present an axiomatic system of probabilistic behaviors, by which we can liberate quantum mechanics from microscopic physics and attain a wide range of applicability of quantum mechanics-type theories, in particular lo human or biological systems. The key concept of this system is the feeling of the object system, which has no relation to the consciousness of an observer in the conventional measurement theory but corresponds directly to the wave function. The probabilistic and logical structures derived from the axioms are discussed. Several examples are taken from classical, quantum, and other models.