Utility Functions for Ceteris Paribus Preferences

Ceteris paribus (all-else equal) preference statements concisely represent preferences over outcomes or goals in a way natural to human thinking. Although deduction in a logic of such statements can compare the desirability of specific conditions or goals, many decision-making methods require numerical measures of degrees of desirability. To permit ceteris paribus specifications of preferences while providing quantitative comparisons, we present an algorithm that compiles a set of qualitative ceteris paribus preferences into an ordinal utility function. Our algorithm is complete for a finite universe of binary features. Constructing the utility function can, in the worst case, take time exponential in the number of features, but common independence conditions reduce the computational burden. We present heuristics using utility independence and constraint-based search to obtain efficient utility functions.     

Utility Models for Goal‐Directed, Decision‐Theoretic Planners

AI planning agents are goal-directed : success is measured in terms of whether an input goal is satisfied. The goal gives structure to the planning problem, and planning representations and algorithms have been designed to exploit that structure. Strict goal satisfaction may be an unacceptably restrictive measure of good behavior, however.
A general decision-theoretic agent, on the other hand, has no explicit goals: success is measured in terms of an arbitrary preference model or utility function defined over plan outcomes. Although it is a very general and powerful model of problem solving, decision-theoretic choice lacks structure, which can make it difficult to develop effective plan‐generation algorithms.
This paper establishes a middle ground between the two models. We extend the traditional AI goal model in several directions: allowing goals with temporal extent, expressing preferences over partial satisfaction of goals, and balancing goal satisfaction against the cost of the resources consumed in service of the goals. In doing so we provide a utility model for a goal-directed agent.
An important quality of the proposed model is its tractability. We claim that our model, like classical goal models, makes problem structure explicit. This structure can then be exploited by a problem-solving algorithm. We support this claim by reporting on two implemented planning systems that adopt and exploit our model.     

A response to comments on ‘Shakespearean tragedies dynamics’ by Bani Dayal Dhir

We discuss the OWA (Ordered Weighted Averaging) operators which provide for aggregation operations lying between the and and the or, and the BADD (BAsic Defuzzification Distribution) transformation. This transformation plays a central role in the development of defuzzification procedures, the aggregation of elements in a fuzzy subset to obtain one representative element. We suggest the use of the BADD transformation to generate a class of OWA like aggregation operators, denoted BADD-OWA operators. We show that while these BADD-OWA operators have some interesting properties, they fail to be mono-tonic. We next use another family of defuzzification operators, called SLIDE, to generate another class of OWA operators. These S-OWA operators provide two subfamilies of OWA operators, one Or-like and the other And-Like.     

A General Framework for Expressing Preferences in Causal Reasoning and Planning

We consider the problem of representing arbitrary preferencesin causal reasoning and planning systems. In planning, a preferencemay be seen as a goal or constraint that is desirable, but notnecessary, to satisfy. To begin, we define a very general querylanguage for histories, or interleaved sequences of world statesand actions. Based on this, we specify a second language inwhich preferences are defined. A single preference defines abinary relation on histories, indicating that one history ispreferred to the other. From this, one can define global preferenceorderings on the set of histories, the maximal elements of whichare the preferred histories. The approach is very general andflexible; thus it constitutes a ‘base’ languagein terms of which higher-level preferences may be defined. Tothis end, we investigate two fundamental types of preferencesthat we call choice and temporal preferences. We consider concretestrategies for these types of preferences and encode them interms of our framework. We suggest how to express aggregatesin the approach, allowing, e.g. the expression of a preferencefor histories with lowest total action costs. Last, our approachcan be used to express other approaches and so serves as a commonframework in which such approaches can be expressed and compared.We illustrate this by indicating how an approach due to Sonand Pontelli can be encoded in our approach, as well as thelanguage PDDL3.     

改进的FAST TCP参数选择方案

针对FAST TCP协议参数选择方案存在的参数选择问题,建立包含窗口更新周期参数和指数滤波环节的FAST TCP网络拥塞控制模型,应用劳思-郝尔维兹稳定判据分析该模型的稳定性,提出一种根据控制器增益参数、窗口更新周期和网络相关参数选择合适协议参数的改进方案以确保FAST TCP系统稳定。理论计算与仿真验证表明,与其他相关方案相比,该改进方案具有更准确的选择范围。     

基于特征偏好的聚类研究

传统的聚类方法,如k均值和模糊c均值,通常并不区分数据特征对聚类的不同贡献或重要度,因此在面对高维数据聚类时,常会导致偏低的聚类性能,这归咎于聚类时未考虑高维数据特征间所存在的高度相关性或冗余.而通过在聚类时为每一特征引入权重并通过聚类目标的优化,不仅能自动获得对应的权重,而且也获得了聚类性能的提升.尽管如此,但无监督获取的特征权重未必吻合用户所期望的特征间的相对重要性(或偏好).因此尝试利用用户给定的实际偏好设计出能反映特征偏好的聚类方法,其将现有独立于个体聚类的全局加权型偏好聚类方法拓展至聚类依赖的局部特征加权型方法,由此弥补了前者的不足,提升了偏好聚类算法的性能.     

一种上下文移动用户偏好自适应学习方法

针对移动网络对个性化移动网络服务系统的性能提出了更高的要求,但现有研究难以自适应地修改上下文移动用户偏好以为移动用户提供实时、准确的个性化移动网络服务的问题,提出了一种上下文移动用户偏好自适应学习方法,在保证精确度的基础上缩短了学习的响应时间.首先,通过分析移动用户行为日志来判断移动用户行为是否受上下文影响,并在此基础上判断移动用户行为是否发生变化.然后,根据判断结果对上下文移动用户偏好进行修正.在对发生变化的上下文移动用户偏好进行学习时,将上下文引入到最小二乘支持向量机中,进一步提出了基于上下文最小二乘支持向量机(C-LSSVM)的上下文移动用户偏好学习方法.最后,实验结果表明,当综合考虑精确度和响应时间两方面因素时,所提出的方法优于其他学习方法,并且可应用于个性化移动网络服务系统中.     

融合偏好度与网络结构的推荐算法

针对传统推荐系统追求推荐列表的准确率而忽略推荐的多样性以及数据集信息缺失等问题，提出了融合偏好度与网络结构的推荐算法。通过用户历史反馈数据分析用户偏好度，将偏好度与二部图随机游走推荐算法融合，初步得出项目推荐列表；利用用户-标签二部图，挖掘用户不跟随大众的喜好标签，得到推荐项目列表；根据模型融合得到最终的推荐结果。实验表明，新算法在保持较好精确率和召回率的情况下，有效提高了推荐的多样性。     

结合特征偏好的半监督聚类学习

半监督聚类是机器学习的重要研究内容之一,它通过利用样本层面的少量标记数据信息或者利用特征层面的特征偏好信息来指导半监督聚类。但现有的半监督聚类算法仅考虑了单一层面的半监督先验信息,罕有同时考虑两个不同层面的此类信息进行半监督聚类。为了弥补这一遗漏,联合利用特征层面给定的特征偏好,即特征之间的相对重要性关系,并结合样本层面的少量标记数据等半监督信息,在传统的半监督聚类算法基础上发展出一个扩展型半监督聚类算法。初步实验验证了该算法的有效性。     

基于双极偏好的滑动窗口参数优化方法

滑动窗口是形状匹配中的常用检测方法,可以检测图像中不同尺度不同位置的多个物体。检测效果采用检测率和误检率来衡量。在传统的滑动窗口检测方法中,通常基于经验选取滑动步长和图像缩放规模这两个参数值,来获得较高的检验率和较低的误检率。然而这是典型的两目标优化问题,传统方法未考虑决策者对检验率与误检率的不同偏好。根据实际情况,考虑到决策者的正偏好(高检验率与低误检率)及负偏好(低检验率和高误检率),引入双极偏好控制策略,提出基于双极偏好的多目标粒子群算法(BPMOPSO)的滑动窗口参数优化方法。通过Leeds Cows图像数据集上图像的检测实验结果表明,与传统算法相比,该算法显著改善了滑动窗口检测中的检验率和误检率,且大大提高了运行效率。     

用户偏好约束的空间关键词范围查询处理方法

随着基于地理位置的个性化服务的广泛应用,用户偏好约束的空间关键词范围查询成为了研究热点。现有面向空间关键词范围查询的索引没有考虑用户偏好属性,导致剪枝性能和查询效率较低。为了解决该问题,提出了一种支持用户偏好属性、空间位置、关键词协同剪枝的混合索引BRPQ;并在此基础上,提出了高效的用户偏好约束的空间关键词范围查询处理算法。实验结果表明,相比现有索引,BRPQ索引的构建时间平均减少了13%,查询效率平均提升了20%。     

融合时间加权信任与用户偏好的协同过滤算法

针对现有的协同过滤推荐算法中存在评分数据稀疏和用户兴趣动态变化的问题,提出了融合时间加权信任与用户偏好的协同过滤算法.考虑到用户评分时间的不均匀,对时间权重进行改进,并将其融入到直接信任计算中,缓解用户兴趣动态变化的问题.通过信任传递得到的间接信任以及建立用户对项目标签的偏好矩阵得到用户之间的偏好相似度来缓解数据的稀疏...     

基于双极偏好占优的阵列天线优化方法

传统进化算法在解决4个或4个以上目标的阵列天线综合问题时,出现了选择压力不足的难题.给出了一种基于双极偏好占优的阵列天线优化设计方法,该方法借助决策者根据解决实际问题的经验给出的目标值偏好,采用TOPSIS方法,比较Pareto解之间的相对贴近度值,建立了严格的非支配关系,引导种群向高的定向辐射方向图及低的零陷值靠近.为了可视化高维空间中的解集,用高维空间对角技术法对高维空间上的解进行可视化,并将该方法与现有的3种多目标优化方法进行解集质量优劣的比较与分析.仿真结果显示,该方法在解决4个以上目标的阵列天线综合问题时具有更好的收敛性以及更多的优秀解个数.     

Reasoning about Actions and Planning with Preferences Using Prioritized Default Theory

This paper shows how action theories, expressed in an extended version of the language     , can be naturally encoded using Prioritized Default Theory . We also show how prioritized default theory can be extended to express preferences between rules . This extension provides a natural framework to introduce different types of preferences in action theories— preferences between actions and preferences between final states . In particular, we demonstrate how these preferences can be expressed within extended prioritized default theory. We also discuss how this framework can be implemented in terms of answer set programming.     

基于用户偏好动态变化的协同过滤推荐

传统的协同过滤方法利用用户评分数据来生成推荐,没有考虑评价时间和项目类别等其他信息,影响了系统推荐的质量。本文提出一种基于用户偏好动态变化的个性化推荐模型,该方法在基于项目类别的基础上,为用户评分时间距离现在较近、较远和周期性评分分别赋予不同的权重。从MovieLens数据集的实验结果表明,该方法消弱了历史短期偏好对推荐质量的影响,准确地反映了用户偏好的动态变化,有效地提高了推荐的准确性。     

Choosing OWA operator weights in the field of Social Choice

One of the most important issues in the theory of OWA operators is the determination of associated weights. This matter is essential in order to use the best-suited OWA operator in each aggregation process. Given that some aggregation processes can be seen as extensions of majority rules to the field of gradual preferences, it is possible to determine the OWA operator weights by taking into account the class of majority rule that we want to obtain when individuals do not grade their pairwise preferences. However, a difficulty with this approach is that the same majority rule can be obtained through a wide variety of OWA operators. For this reason, a model for selecting the best-suited OWA operators is proposed in this paper.     

相似性的二值表示

在模式识别与机器学习领域,相似性具有重要作用.但是相似性具有不同的解释.讨论了相似性在原型理论、样例理论下的不同解释,指出几乎所有的非负度量都有对应的相似性解释,说明了一定程度上相似性反映了对象的全局性质.作为一般相似性的例子,给出了图像、模糊集合的相似性解释,指出模糊集合是研究论域内对象与概念相似性的有效工具之一,并根据韦特海默对比不变性原则(Wertheimers contrast invariant principle),导出了相似对比不变性准则.据此建立了有界非负矩阵的二值表示.这些结果可以得到相似矩阵的最优二值分解.由于相似性的广泛性,该模型可望有很多应用.     

Data Mining by Means of Binary Representation: A Model for Similarity and Clustering

In this paper we outline a new method for clustering that is based on a binary representation of data records. The binary database relates each entity to all possible attribute values (domain) that entity may assume. The resulting binary matrix allows for similarity and clustering calculation by using the positive (1 bits) of the entity vector. We formulate two indexes: Pair Similarity Index (PSI) to measure similarity between two entities and Group Similarity Index (GSI) to measure similarity within a group of entities. A threshold factor for each attribute domain is defined that is dependent on the domain but independent of the number of entities in the group. The similarity measure provides simplicity of storage and efficiency of calculation. A comparison of our similarity index to other indexes is made. Experiments with sample data indicate a 48% improvement of group similarity over standard methods pointing to the potential and merit of the binary approach to clustering and data mining.     

Some realizations and instances of Yager prioritized preference frame with application in evaluation and decision making

This study first revamps Yager prioritized ordered weighted averaging operators, and condenses them into a conceptual frame with putting aside one realization from Yager's original proposal. Then, based on elicited conceptual frame called Yager prioritized preference conceptual frame, this article proposes three distinct realizations to the conceptual frame with corresponding different instances, in which some evaluation models with weights determination methods are provided. Numerical examples are also presented immediately after every instance. Lastly, this study proposes the concepts of outer monotonic, inner monotonic, and global monotonic weights functions, and discusses some related properties, which are often embodied in preferences of decision makers.