有限理性模型在游戏智能决策中的研究与应用*

为了让游戏NPC能够学习和模拟玩家在游戏中的策略和行为方式,在基于模型的智能决策方法基础上,结合行为决策理论中的有限理性模型提出了一种新的游戏智能方法.该方法分别从有限理性模型的两个核心原则——有限理性和满意准则来改进过去的方法在感知和决策过程中所面对的问题,从而使得游戏NPC行为决策方式更加人性化.最后,通过在Starcraft平台上与其他方法的对抗性实验来进一步验证该方法的优势.     

Computational intelligence determines effective rationality

Rationality is a fundamental concept in economics. Most researchers will accept that human beings are not fully rational. Herbert Simon suggested that we are ＂bounded rational＂. However, it is very difficult to quantify ＂bounded rationality＂, and therefore it is difficult to pinpoint its impact to all those economic theories that depend on the assumption of full rationality. Ariel Rubinstein proposed to model bounded rationality by explicitly specifying the decision makers＇ decision-making procedures. This paper takes a computational point of view to Rubinstein＇s approach. From a computational point of view, decision procedures can be encoded in algorithms and heuristics. We argue that, everything else being equal, the effective rationality of an agent is determined by its computational power - we refer to this as the computational intelligence determines effective rationality （CIDER） theory. This is not an attempt to propose a unifying definition of bounded rationality. It is merely a proposal of a computational point of view of bounded rationality. This way of interpreting bounded rationality enables us to （computationally） reason about economic systems when the full rationality assumption is relaxed.     

An evolutionary game algorithm for grid resource allocation under bounded rationality

Resource allocation cannot reach equilibrium in one‐off game in grid environment because of the bounded rationality of the users. To address this issue, an evolutionary game algorithm for grid resource allocation is proposed in this paper. The evolutionary game theory is introduced to study the selection process of user strategy from the dynamic viewpoint. Firstly, the problem of multiple users competing for a common resource is formulated as a symmetric game. Secondly, replicated dynamic mechanism is used to produce the evolutionary stable point that leads to a satisfied allocation scenario. Finally, the relationships among the evolutionary stable point, valuation functions, and convergence time are discussed in detail. The results of the experiments show that the proposed evolutionary game algorithm is convergent and generates better utility results compared with the classical game algorithm. Copyright © 2009 John Wiley & Sons, Ltd.     

基于有限理性的弹性需求随机用户均衡交通分配模型*

为了同时考虑路网的随机变化特征和出行者的感知误差,在有限理性框架下基于累积前景理论建立了一个弹性需求随机用户均衡模型,给出了等价的变分不等式,设计了求解算法并通过算例进行了验证,结合参数敏感性对均衡状态出行者的认知和心理特征进行了分析。结果表明,OD出行需求和网络均衡态具有显著的参照点依赖效应,出行者对路况满意度越高OD出行需求越大,对路况熟悉程度越高OD出行需求越小。模型及算法可以加深对出行行为的理解,改进传统模型理论假设及适用性的局限,更加精确描述交通流的实际分布形态。     

有限理性下知识团队知识共享的演化博弈分析

为提高知识团队成员共享知识的意愿,通过运用演化博弈论,对知识团队知识共享的内在机理与动态演化过程进行了分析.结果表明,影响知识团队知识共享的因素有知识员工的知识拥有量差距、风险系数、知识共享度、互补性知识比例、激励系数、协同性影响程度等,通过调整这些参数的大小,可以有效提高知识团队成员选择知识共享策略的概率.     

Games Machines Play

Individual rationality, or doing what is best for oneself, is a standard model used to explain and predict human behavior, and von Neumann–Morgenstern game theory is the classical mathematical formalization of this theory in multiple-agent settings. Individual rationality, however, is an inadequate model for the synthesis of artificial social systems where cooperation is essential, since it does not permit the accommodation of group interests other than as aggregations of individual interests. Satisficing game theory is based upon a well-defined notion of being good enough, and does accommodate group as well as individual interests through the use of conditional preference relationships, whereby a decision maker is able to adjust its preferences as a function of the preferences, and not just the options, of others. This new theory is offered as an alternative paradigm to construct artificial societies that are capable of complex behavior that goes beyond exclusive self interest.     

动态服务的数据交换模型研究*

数据交换是解决信息孤岛问题的实用方法,但如何实施灵活有效的数据交换一直是研究的难点.基于此,提出了动态服务的数据交换方式来克服原有单一、固定的交换缺点.依据交换系统的现状,首先给出了静态服务库、动态服务库和静态交换系统、动态交换系统的概念;然后提出了动态服务的交换模型,并给出了交换的方法、原则和实现过程.最后,结合具体的应用实例,验证了模型的灵活性和有效性.     

Limited Rationality in Action: Decision Support for Military Situation Assessment

Information is a force multiplier. Knowledge of the enemy's capability and intentions may be of far more value to a military force than additional troops or firepower. Situation assessment is the ongoing process of inferring relevant information about the forces of concern in a military situation. Relevant information can include force types, firepower, location, and past, present and future course of action. Situation assessment involves the incorporation of uncertain evidence from diverse sources. These include photographs, radar scans, and other forms of image intelligence, or IMINT; electronics intelligence, or ELINT, derived from characteristics (e.g., wavelength) of emissions generated by enemy equipment; communications intelligence, or COMINT, derived from the characteristics of messages sent by the enemy; and reports from human informants (HUMINT). These sources must be combined to form a model of the situation. The sheer volume of data, the ubiquity of uncertainty, the number and complexity of hypotheses to consider, the high-stakes environment, the compressed time frame, and deception and damage from hostile forces, combine to present a staggeringly complex problem. Even if one could formulate a decision problem in reasonable time, explicit determination of an optimal decision policy exceeds any reasonable computational resources. While it is tempting to drop any attempt at rational analysis and rely purely on simple heuristics, we argue that this can lead to catastrophic outcomes. We present an architecture for a ``complex decision machine' that performs rational deliberation to make decisions in real time. We argue that resource limits require such an architecture to be grounded in simple heuristic reactive processes. We thus argue that both simple heuristics and complex decision machines are required for effective decision making in real time for complex problems. We describe an implementation of our architecture applied to the problem of military situation assessment.     

The influence of user cognition on consumption decision-making from the perspective of bounded rationality

Consumer decision-making is related to the success or failure of enterprises, and products that cater to the cognitive preferences of users have become a focus of current research. Based on the theory of bounded rationality, this paper explores the cognitive process of consumer decision-making. Then, how product shape affects consumption decision-making is analyzed with eye-tracking technology. Finally, the design principle of the product form is further explored. The results demonstrate the following: (1) the perceptual cognition of users has a driving effect on consumption behavior; (2) as a key factor affecting the perceptual cognition of users, product form affects consumer decision-making by influencing the degree of approach motivation; (3) by establishing the mapping relationship between product form elements and user images, the principles of product form design can be more consistent with user image preferences. This study provides useful suggestions for how to increase the purchase behavior of users from the perspective of bounded rationality.     

有限理性下的企业合作竞争的进化博弈分析

在有限理性的条件下,应用进化博弈论中的模仿者动态模型来分析企业合作竞争博弈的演化,用进化稳定策略来描述合作竞争博弈的长期演化趋势。并分别讨论了对称和非对称的合作竞争博弈的进化均衡与稳定性。最后,指出确定性模仿者动态模型的不足,以及当今随机动态系统理论的发展。     

Existence of perfect equilibria in a class of multigenerational stochastic games of capital accumulation

In this paper we introduce a model of multigenerational stochastic games of capital accumulation where each generation consists of m different players. The main objective is to prove the existence of a perfect stationary equilibrium in an infinite horizon game. A suitable change in the terminology used in this paper provides (in the case of perfect altruism between generations) a new Nash equilibrium theorem for standard stochastic games with uncountable state space.     

面向电动汽车的分布式储能主动汇聚与协同优化方法研究

分布式储能在支撑原有配置需求的基础上具有一定的闲置容量和闲置时间, 若能汇聚这部分闲置储能形成规模化的储能集群, 将能为电网提供多类型辅助服务. 以电动汽车(EV)这一特殊分布式储能为例, 研究考虑个体有限理性特征下的微电网运营商(MGO)对主动电动汽车的汇聚方案: 首先, 建立含主动EV的系统架构, 设计了微电网营运商与主动EV之间的互动流程; 然后, 基于EV的实际出行需求, 利用灵活充放电范围理论建立统一化的EV约束模型; 考虑现实EV用户的有限理性特征, 采用前景理论对其进行有限理性建模; 最后, 将MGO和主动EV之间的互动过程建立为斯塔克伯格博弈(Stackelberg game)模型并验证了博弈均衡解的存在性和唯一性. 算例基于一个商/居型并网微网, 验证了所提模型能有效汇聚分布式EV储能, 促进可再生能源发电就地消纳     

Robust satisficing and the probability of survival

Concepts of robustness are sometimes employed when decisions under uncertainty are made without probabilistic information. We present a theorem that establishes necessary and sufficient conditions for non-probabilistic robustness to be equivalent to the probability of satisfying the specified outcome requirements. When this holds, probability is enhanced (or maximised) by enhancing (or maximising) robustness. Two further theorems establish important special cases. These theorems have implications for success or survival under uncertainty. Applications to foraging and finance are discussed.     

基于随机博弈与禁忌搜索的网络防御策略选取

网络防御策略是决定网络安全防护效果的关键因素,现有的网络防御决策研究的是完全理性前提条件以及攻防效益函数参数选择等方面,对实际网络攻防中信息不对称、法律惩戒等因素存在模型偏差,降低了策略的实用性与可靠性.结合实际问题,在有限理性的前置条件基础上构建禁忌随机博弈模型,引入了禁忌搜索方法对随机博弈进行有限理性的分析,并设计具有记忆功能的搜索方法,通过禁忌表数据结构实现记忆功能,并利用数据驱动的记忆结合博弈模型得出最优防御策略.实验结果表明：该方法在攻防收益量化方面提高了精准度,防御效益相对于现有典型的方法提高了准确度,方法空间复杂度优于强化学习等典型方法.     

关于安全案例论证构建的综述

安全案例提供清晰、全面和可靠的论据,说明系统在特定环境下的操作满足可接受的安全性.在受监管的安全攸关领域,如汽车、航空和核能等领域,认证机构通常要求系统经过严格的安全评估程序,以确保其符合一个或多个安全标准.在系统开发中应用安全案例是一种新兴的技术手段,以结构化和全面的方式表达安全攸关系统的安全属性.我们对安全案例的四个基本构建步骤—确定目标、收集证据、构建论证和评估安全案例—进行简要介绍.然后聚焦于构建论证这一关键步骤,详细介绍现有的8种安全案例表达形式,包括目标结构符号(GSN)、声明-论点-证据(CAE)、结构化安全案例元模型(SACM)等,并分析了它们的优缺点.由于安全案例所需材料的显著复杂性,软件工具通常被用作构建和评估安全案例的实用方法.我们比较7种用于安全案例开发和评估的工具,包括Astah System Safety、gsn2x、NOR-STA、Socrates、ASCE、D-Case Editor和AdvoCATE.此外,本文还深入探讨了安全案例构建中所面临的多重挑战,这些挑战包括数据的可靠性和完整性、复杂性和不确定性的管理、监管和标准的不一致、人因工程、技术的快速发展以及团队和跨学科合作6个方面.最后,我们展望安全案例的未来研究方向,揭示其潜在应用和研究问题.     

Satisficing Equilibria: A Non-Classical Theory of Games and Decisions

Satisficing, or being good enough, is the fundamental obligation of rational decision makers. We cannot rationally choose an option, even when we do not know of anything better, unless we know it is good enough. Unfortunately, we are not often in the position of knowing that there could be no better option, and hence that the option must be good enough. A complete search through all logical possibilities is often impractical, particularly in multi-agent contexts, due to excessive computational difficulty, modeling complexity, and uncertainty. It can be equally impractical, if it is even possible, to determine the cost of the additional required search to find an option that is good enough. In a departure from the traditional notion of satisficing as a species of bounded rationality, satisficing is here redefined in terms of a notion of intrinsic rationality. Epistemic utility theory serves as the philosophical foundation of a new praxeological decision-making paradigm of satisficing equilibria that is applicable to both single- and multiple-agent scenarios. All interagent relationships are modeled by an interdependence function that explicitly accommodates both self and group interest, from which multilateral and unilateral selectability and rejectability mass functions can be derived and compared via the praxeic likelihood ratio test.     

考虑排污权交易的有限理性企业博弈模型研究

在有限理性基础上建立寡头垄断博弈模型,将其引入到排污权市场中,同时考虑了生产成本、污染治理成本、排污权交易价格的影响,使该模型更符合实际。将不同理性层次,不同结构的非线性成本函数,不完全信息等因素引入到博弈模型中,对改进后的模型的演化过程进行分析,找出博弈均衡点,并分析其稳定性。由于具有有限理性的双寡头Cournot博弈模型会产生丰富的动力学行为,因此对其进行数据模拟后并对混沌现象进行分析。在此基础上,运用延迟反馈控制法对寡头垄断博弈模型的混沌控制进行了解析分析,结果表明选择合适的控制因子可使模型稳定在Nash均衡。