The Local Geometry of Multiattribute Tradeoff Preferences

Existing representations for multiattribute ceteris paribus preference statements have provided useful treatments and clear semantics for qualitative comparisons, but have not provided similarly clear representations or semantics for comparisons involving quantitative tradeoffs. We use directional derivatives and other concepts from elementary differential geometry to interpret conditional multiattribute ceteris paribus preference comparisons that state bounds on quantitative tradeoff ratios. This semantics extends the familiar economic notion of marginal rate of substitution to multiple continuous or discrete attributes. The same geometric concepts also provide means for interpreting statements about the relative importance of different attributes.     

Internet-based e-shopping and consumer attitudes: an empirical study

This paper analyses consumer attitudes towards Internet-based e-shopping. It aims to provide a theoretically and empirically grounded initial reference position, against which later research can explore and interpret the effects of changes in variables representing consumer preferences and shifts in these preferences on the success or failure of B2C e-commerce over the Internet. Because of the opportunity to sample at the outset and of ceteris paribus conditions following from the tendency for other factors such as e-transactions cost to remain small and constant, Singapore data were employed. Regression analysis shows that the life content of products, transactions security, price, vendor quality, IT education and Internet usage significantly affect the initial willingness of Singaporeans to e-shop on the Internet. Generalising, we suggest that Internet-based B2C e-commerce can profitably be introduced or promoted along similar dimensions in socio-geographically and technologically similar situations.     

Towards a Possibilistic Logic Handling of Preferences

The classical way of encoding preferences in decision theory is by means of utility or value functions. However agents are not always able to deliver such a function directly. In this paper, we relate three different ways of specifying preferences, namely by means of a set of particular types of constraints on the utility function, by means of an ordered set of prioritized goals expressed by logical propositions, and by means of an ordered set of subsets of possible choices reaching the same level of satisfaction. These different expression modes can be handled in a weighted logical setting, here the one of possibilistic logic. The aggregation of preferences pertaining to different criteria can then be handled by fusing sets of prioritized goals. Apart from a better expressivity, the benefits of a logical representation of preferences are to put them in a suitable format for reasoning purposes, or for modifying them.     

CP-nets的定性偏好决策及一致性推理

CPnets是一种简单而又直观的图形化偏好表示工具,特别适合描述不完全信息下的具有依赖关系的多属性 定性偏好决策。首先通过构造CP-net s导出图及对其性质的研究,得出强占优测试本质上是导出图上顶点之间的可 达性问题,从而利用图的深度优先通历算法实现了二值网的强占优测试;然后分别从无环图、有环图的角度给出CP- nets一致性的相关定理和性质,提出了判断一致性的3种方法,使得CP-nets的一致性问题得到解决;强化和扩充了 I3outilier所提出的一些概念,深化了CP-net s的基础理论研究。     

利用派生谓词和偏好处理OSP 问题的目标效益依赖

在过渡规划问题(over-subscribed planning,简称 OSP)研究中,如果目标之间不是相互独立的,那么目标坚定效益依赖比单个目标效益更能提高规划解的质量.但是,已有的描述模型不符合标准规划描述语言(planning domain descrion language,简称PDDL)的语法规范,不能在一般的OSP规划系统上进行推广,提出了用派生谓词规则和目标偏好描述效益依赖的方法,这二者均为ＰDDL语言的基本要素.实质上,将已有的GAI模型转化为派生谓词规则和目标偏好,其中派生谓词规则显式描述目标子集的存在条件,偏好机制用来表示目标子集的效益,二者缺一不可.该转换算法既可以保持在描述依赖关系时GAI模型的易用性和直观性上,又可以扩展一般的OSP规划系统处理目标效益依赖的能力.从理论上可以证明该算法在转化过程中的语义不变性,子啊基准领域的实验结果表明其可行性和规划解质量的改善能力.提出符合PDDL语言规范的目标效益依赖关系的描述形式,克服了已有模型不通用的缺点.     

Efficient and non-parametric reasoning over user preferences

We consider the problem of modeling and reasoning about statements of ordinal preferences expressed by a user, such as monadic statement like “X is good,” dyadic statements like “X is better than Y,” etc. Such qualitative statements may be explicitly expressed by the user, or may be inferred from observable user behavior. This paper presents a novel technique for efficient reasoning about sets of such preference statements in a semantically rigorous manner. Specifically, we propose a novel approach for generating an ordinal utility function from a set of qualitative preference statements, drawing upon techniques from knowledge representation and machine learning. We provide theoretical evidence that the new method provides an efficient and expressive tool for reasoning about ordinal user preferences. Empirical results further confirm that the new method is effective on real-world data, making it promising for a wide spectrum of applications that require modeling and reasoning about user preferences.     

Graphically structured value-function compilation

Classical work on eliciting and representing preferences over multi-attribute alternatives has attempted to recognize conditions under which value functions take on particularly simple and compact form, making their elicitation much easier. In this paper we consider preferences over discrete domains, and show that for a certain class of simple and intuitive qualitative preference statements, one can always generate compact value functions consistent with these statements. These value functions maintain the independence structure implicit in the original statements. For discrete domains, these representation theorems are much more general than previous results. However, we also show that it is not always possible to maintain this compact structure if we add explicit ordering constraints among the available outcomes.     

CP-nets的可满足性及一致性研究

CP-nets是一种简单而又直观的图形化偏好表示工具,成为近几年人工智能的一个研究热点,然而对于CP-nets的可满足性和一致性等相关性质的研究还很欠缺.既没有给出严格的定义,也没有探讨不同性质之间的联系,没有一个求可满足性序列的通用算法.从研究CP-nets的可满足性和一致性的关系着手,得出了任意结构二值CP-nets的可满足性判定算法及可满足性序列生成算法.首先通过构造CP-nets导出图及其性质的研究,得出CP-nets的可满足性及一致性的相关定理.再把不同性质结合起来分析,给出CP-nets可满足性等价于一致性的结论,从而利用拓扑排序的思想实现了任意结构二值CP-nets的可满足性序列的生成.强化和扩充了Boutilier所提出的一些概念,深化了CP-nets的基础理论研究.     

Estimating the Functional Form of Road Traffic Maturity

It has been observed that older high traffic motorways experience lower traffic growth than newer ones (ceteris paribus). This phenomenon is known as traffic maturity; however, it is not captured through traditional time-series long-term forecasts, due to constant elasticity to gross domestic product these models assume. In this paper we argue that traffic maturity results from decreasing marginal utility of transport. The elasticity of individual mobility with respect to the revenue tends to decrease when the level of mobility increases. In order to find evidences of decreasing elasticity we analyse a cross-section time-series sample including 40 French motorways’ sections. This analysis shows that decreasing elasticity can be observed in the long term. We then propose a decreasing function for the traffic elasticity with respect to the economic growth, which depends on the traffic level on the road. This model provides a good explanation for the observed traffic evolution and gives a rigorous econometric approach to time-series traffic forecasts.     

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.     

Optimal paths in probabilistic networks: A case with temporary preferences

The classical shortest route problem in networks assumes deterministic arc weights and a utility (or cost) function that is linear over path weights for route evaluation. When the environment is stochastic and the “traveler's” utility function for travel attributes is nonlinear, we define “optimal paths” that maximize the expected utility.We review the concepts of temporary and permanent preferences for comparing a traveler's preference for available subpaths. It has been shown before that when the utility function is linear or exponential, permanent preferences prevail and an efficient Dijkstra-type algorithm [3] is available that determines the optimal path.In this paper an exact procedure is developed for determining an optimal path when the utility function is quadratic—a case where permanent preferences do not always prevail. The algorithm uses subpath comparison rules to establish permanent preferences, when possible, among subpaths of the given network. Although in the worst case the algorithm implicitly enumerates all paths (the number of operations increasing exponentially with the size of the network), we find, from the computational experience reported, that the number of potentially optimal paths to evaluate is generally manageable.     

Conditional Preference Networks with User's Genuine Decisions

User's choices involve habitual behavior and genuine decision. Habitual behavior is often expressed using preferences. In a multiattribute case, the Conditional Preference Network (CP-net) is a graphical model to represent user's conditional ceteris paribus (all else being equal) preference statements. Indeed, the CP-net induces a strict partial order over the outcomes. By contrast, we argue that genuine decisions are environmentally influenced and introduce the notion of “comfort” to represent this type of choices. In this article, we propose an extension of the CP-net model that we call the CP-net with Comfort (CPC-net) to represent a user's comfort with preferences. Given that preference and comfort might be two conflicting objectives, we define the Pareto optimality of outcomes when achieving outcome optimization with respect to a given CPC-net. Then, we propose a backtrack search algorithm to find the Pareto optimal outcomes. On the other hand, two outcomes can stand in one of six possible relations with respect to a CPC-net. The exact relation can be obtained by performing dominance testing in the corresponding CP-net and comparing the numeric comforts.     

Organizing for Continuous Innovation: On the Sustainability of Ambidextrous Organizations

Organizing for innovation does not present itself as a straightforward exercise. The complexities entailed when implementing an innovation strategy can be related directly to the multitude of objectives it comprises. Recently, several scholars have advanced the notions of semi‐ or quasi‐structures and ambidextrous organizations to handle these multiple requirements. These organizational forms imply the simultaneous presence of different activities, exhibiting differences in technology and market maturation. As a consequence, financial returns will reflect this diversified resource allocation pattern. Moreover, as higher levels of complexity are being introduced; ambidextrous organizations will encounter additional, organizational, costs. Compared to organizations that focus on the most profitable part of the portfolio, ambidextrous organizations – ceteris paribus – tend to be inferior in terms of financial returns. Within this contribution we explore under which conditions ambidextrous organizations can outperform focused firms; considered a prerequisite for their sustainability. In order to do so, we develop an analytical framework depicting the differential value dynamics, focused and ambidextrous firms can enact. Our findings reveal the relevancy of adopting extended time frames as well as introducing interface management practices aimed at cross‐fertilization. Finally, the synergetic potential of (underlying) technologies comes to the forefront as necessary in order for ambidextrous organizations to become sustainable.     

CP-nets及其表达能力研究

偏好处理是人工智能中的一个重要研究内容, 它的4个研究热点是偏好的表示、 提取、 聚合和推理. 条件偏好网(Conditional preference networks, CP-nets)是一种简单直观的偏好表示的图形工具, 但很少有工作研究CP-nets的表达能力. 本文研究CP-nets的表达能力, 详细研究了CP-nets表达偏好的完备性, 其上构造的运算复杂度以及适用的场合. 首先给出了CP-nets模型上的几个运算, 利用改进的Warshall算法求出了二值网的强占优测试在最坏情况下的复杂度为O(4n). 其次通过构造CP-nets导出图及其性质的研究, 得出CP-nets特别适合不完全信息下的多属性定性偏好决策. 当需要处理更完全信息时, 可借助于与Agent的交互来完成. 虽然我们给出了CP-nets的强占优测试的理论解, 但其理论上可解, 实际上不可解. 为了解决强占优测试的指数级复杂度问题, 本文最后给出了一种带有软约束的满足问题(Soft constraint satisfaction problem, SCSP)的求解方法. 它把CP-nets中的定性运算转为约束半环中的定量运算, 从而将指数级的复杂度转化为多项式的复杂度, 间接提高了部分CP-nets的表达能力. 本文所做的工作是对Boutilier和Bistarelli工作的改进和提高.     

带有目标偏好的最大成功联盟生成算法*

资源结盟博弈(CRGs)研究均假设每个agent可以响应所有目标,即使目标不在其感兴趣的子目标集内.针对此问题,文中提出带有目标偏好的CRGs模型,即每个agent只愿意把自己的有限资源贡献给自己的兴趣集中的目标.此外,设计基于二维二进制编码的最大成功联盟生成算法,并提出编码修正启发式算法解决多个目标竞争同一agent资源可能引起的的资源冲突.最后,通过与已有相关算法的对比实验验证文中算法的有效性.     

Compactly representing utility functions using weighted goals and the max aggregator

Weighted propositional formulas can be used to model preferences over combinatorial domains: each formula represents a goal we would like to see satisfied, the weight of a formula represents the importance of the goal in question, and to assess the desirability of a given alternative we aggregate the weights of the goals satisfied by that alternative. One of several options is to aggregate by using the maximum of the weights of the satisfied goals. This approach gives rise to a family of preference representation languages, one for each of a range of possible restrictions we can impose on either formulas or weights. We analyze the properties of these languages and establish results regarding their expressivity, and absolute and relative succinctness. We also study the computational complexity of the problem of finding the best and the worst alternative for a given set of weighted goals, and of finding an alternative that is optimal for a group of agents, for a range of different notions of collective optimality proposed in social choice theory and welfare economics.     

基于遗传算法的多响应优化方法研究

满意度函数法应用优化算法对总体满意度函数最大化求解,获得最佳因子组合。然而,满意度函数有时不可微,随因子、响应个数的增加,问题变复杂时,传统的优化算法可能获得局部最优解。提出一种基于遗传算法的满意度多响应优化方法。实例验证了该方法的有效性。     

一种结合独立性模型与差异评估的Co-Training改进方案

Co-Training算法要求两个特征视图满足一致性和独立性,但是,许多应用中不存在自然划分且满足这种假设的两个视图.为此,提出利用互信息（MI）或者CHI统计量评估特征之间的相互独立性,建立特征相互独立性模型（MID-Model）.基于该模型,提出了新的特征子集划分方法PMID-MI与PMID-CHI算法,能有效地将一个特征集合划分成两个独立性较强的子集.并且利用多种差异评估法,进一步验证两个子集的独立性.基分类器之间的差异性能够减少两个基分类器给同一个未标注文本都标注错误的可能性.最后,提出了对Co-Training的改进算法SC-PMID.实验结果表明SC-PMID算法能够明显提高半监督分类精度.     

MERGING SEPARATELY GENERATED PLANS WITH RESTRICTED INTERACTIONS

Generating action sequences to achieve a set of goals is a computationally difficult task. When multiple goals are present, the problem is even worse. Although many solutions to this problem have been discussed in the literature, practical solutions focus on the use of restricted mechanisms for planning or the application of domain dependent heuristics for providing rapid solutions (i.e., domain-dependent planning). One previously proposed technique for handling multiple goals efficiently is to design a planner or even a set of planners (usually domain-dependent) that can be used to generate separate plans for each goal. The outputs are typically either restricted to be independent and then concatenated into a single global plan, or else they are merged together using complex heuristic techniques. In this paper we explore a set of limitations, less restrictive than the assumption of independence, that still allow for the efficient merging of separate plans using straightforward algorithmic techniques.
In particular, we demonstrate that for cases where separate plans can be individually generated, we can define a set of limitations on the allowable interactions between goals that allow efficient plan merging to occur. We propose a set of restrictions that are satisfied across a significant class of planning domains. We present algorithms that are efficient for special cases of multiple plan merging, propose a heuristic search algorithm that performs well in a more general case (where alternative partially ordered plans have been generated for each goal), and describe an empirical study that demonstrates the efficiency of this search algorithm.     

An agent specific planning algorithm

Planning algorithms are often applied by intelligent agents for achieving their goals. For the plan creation, this kind of algorithm uses only an initial state definition, a set of actions, and a goal; while agents also have preferences and desires that should to be taken into account. Thus, agents need to spend time analyzing each plan returned by these algorithms to find one that satisfies their preferences. In this context, we have studied an alternative in which a classical planner could be modified to accept a new conceptual parameter for a plan creation: an agent mental state composed by preferences and constraints. In this work, we present a planning algorithm that extends a partial order algorithm to deal with the agent’s preferences. In this way, our algorithm builds an adequate plan in terms of agent mental state. In this article, we introduce this algorithm and expose experimental results showing the advantages of this adaptation.