基于约束满足的相似时序数据的搜索算法

提出了一种基于约束满足的启发式搜索方法,用于寻找时序数据相似变化区域。该算法利用所给出的一个局部约束条件和３个全局约束条件,从整体上模仿人对相似时序数据的模糊判断过程,以此找出数据序列中相似的变化区域。并给出了该处地太阳黑子和证券交易Ｃｏｍｐｏｓｉｅｔｅ指数数据的实验测试结果。     

一个线性的启发式图搜索算法

本文描述了一个能够改善启发函数的启发式图搜索算法．它利用搜索过程中的信息,改动启发函数h,保持搜索树上始终满足单调限制条件,使算法的最坏复杂度从B′的O（N²）（L.Méró,1984）降为O（N）．本文还证明了新算法的可采纳性、线性的复杂度,并同算法B′作了性能比较．     

Interactive computer support in decision conferencing: Two cases on off-site nuclear emergency management

In this paper, we study the new opportunities offered by the interactive use of advanced multicriteria software in decision conferencing. We analyze and make observations on this approach in two one-day decision conferences on the planning of later phase countermeasures in off-site nuclear emergency management. The participants' individual use of the software in the preference elicitation phase was an essential new characteristic of these conferences. It turned out to be a feasible and well accepted process. We shall discuss the requirements for the facilitation and computer support in this kind of an approach.     

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.     

多因素问题的启发式搜索算法MFRA

本文新定义了一类多因素启妇式搜索问题，提出了适于此类问题求解的启发式搜索算法ＭＦＲＡ。文中研究了算法ＭＦＲＡ的可采纳性质，单调限制性质和比较性质等。基于算法ＩＤＡ的思想，提出了ＭＦＲＡ的改进算法ＭＦＲＡ－ＩＤＡ，这一算法具有线性存储空间这一重要特性。     

Approximating the Pareto curve with local search for the bicriteria TSP(1,2) problem

Local search has been widely used in combinatorial optimization (Local Search in Combinatorial Optimization, Wiley, New York, 1997), however, in the case of multicriteria optimization almost no results are known concerning the ability of local search algorithms to generate “good” solutions with performance guarantee. In this paper, we introduce such an approach for the classical traveling salesman problem (TSP) problem (Proc. STOC’00, 2000, pp. 126–133). We show that it is possible to get in linear time, a -approximate Pareto curve using an original local search procedure based on the 2-opt neighborhood, for the bicriteria TSP(1,2) problem where every edge is associated to a couple of distances which are either 1 or 2 (Math. Oper. Res. 18 (1) (1993) 1).     

JUDGES: A descriptive group decision support system for the ranking of items

A set of tools for group decision support are presented. Decision problems involving several decision makers, here-after called judges, that have to rank several alternatives, are considered. The toolbox is called JUDGES. It includes the four following procedures:

• - a hierarchical representation of the judges allows to display the existing conflicts between groups of judges,

• - enhanced box-plots representations of the alternatives are generated in order to detect those that are responsible for the major conflicts,

• - specific advice is issued to each judge in order to reach more easily a consensus,

• - a general framework for a pairwise group preference structure is proposed, and can be used to finalise the decision.

These procedures are embedded in an interactive software, implemented on micro-computer, which currently simulates the use on a network. Actual network implementation is foreseen in the near future. Several applications are presented and future developments are discussed.

State Space Search with Prioritised Soft Constraints

This paper addresses two issues: how to choose between solutions for a problem specified by multiple criteria, and how to search for solutions in such situations. We argue against an approach common in decision theory, reducing several criteria to a single cost (e.g., using a weighted sum cost function) and instead propose a way of partially ordering solutions satisfying a set of prioritised soft constraints. We describe a generalisation of the A^* search algorithm which uses this ordering and prove that under certain reasonable assumptions the algorithm is complete and optimal.     

A flexible consensus scheme for multicriteria group decision making under linguistic assessments

The present paper proposes a flexible consensus scheme for group decision making, which allows one to obtain a consistent collective opinion, from information provided by each expert in terms of multigranular fuzzy estimates. It is based on a linguistic hierarchical model with multigranular sets of linguistic terms, and the choice of the most suitable set is a prerogative of each expert. From the human viewpoint, using such model is advantageous, since it permits each expert to utilize linguistic terms that reflect more adequately the level of uncertainty intrinsic to his evaluation. From the operational viewpoint, the advantage of using such model lies in the fact that it allows one to express the linguistic information in a unique domain, without losses of information, during the discussion process.The proposed consensus scheme supposes that the moderator can interfere in the discussion process in different ways. The intervention can be a request to any expert to update his opinion or can be the adjustment of the weight of each expert’s opinion. An optimal adjustment can be achieved through the execution of an optimization procedure that searches for the weights that maximize a corresponding soft consensus index.In order to demonstrate the usefulness of the presented consensus scheme, a technique for multicriteria analysis, based on fuzzy preference relation modeling, is utilized for solving a hypothetical enterprise strategy planning problem, generated with the use of the Balanced Scorecard methodology.     

Quality of data model for supporting mobile decision making

This paper describes research towards implementation of a mobile decision support system. Our view is that the mobile decision maker will benefit if provided with a measure of the Quality of the Data (QoD) used in deriving a decision, and how QoD improves or deteriorates while he/she is on the move. We propose a QoD model taking into account static and dynamic properties of the mobile decision context, and use multicriteria decision analysis to represent decision model and derive a QoD measure. A prototype mobile decision support system has been developed to investigate the usefulness of the proposed QoD model.     

宽度优先反复加宽及其启发式搜索算法研究

首先针对搜索树中深度固定且目标唯一的寻优问题,指出宽度优先反复加宽的搜索效率要比深度优先反复加深的搜索效率高,基于此,提出了基于宽度优先反复加宽的启发式搜索算法ＩＷＡ＊,算法ＩＷＡ＊是可采纳的。为了保持算法ＩＷＡ＊的搜索效率高于算法ＩＤＡ＊的搜索效率,同时又使算法ＩＷＡ＊的存贮空间复杂度减低,文中基于分层技术,提出了基于深度优先的ＩＷＡ＊算法──ＩＤＷＡ＊。算法ＩＤＷＡ＊也是一个可采纳的启发式搜索算法。     

蚁群算法及其实现方法研究

蚁群算法是一种相对较新的启发式方法,通过模拟蚂蚁的觅食行为解决问题,是目前昆虫算法中较成功的例子.蚁群算法的本质是一种并行的、自组织的算法,它可应用于更好地组织大数目实体的相互作用过程,如货郎担问题、车辆绕径问题、排程问题等。该文简述了蚁群算法的起源和发展,总结了蚁群算法的特点和不足及针对这些不足提出的各种改进方法,并介绍了和蚁群算法相关的几种具体应用。最后,文章探讨了蚁群算法研究中仍存在的问题和以后的发展方向。     

领域相关的Web网站抓取方法

本文提出了一种抓取领域相关的Web站点的方法,可以在较小的代价下准确地收集用户所关心领域内的网站。这种方法主要改进了传统的聚焦爬虫（Focused Crawler）技术,首先利用Meta-Search技术来改进传统Crawler的通过链接分析来抓取网页的方法,而后利用启发式搜索大大降低了搜索代价,通过引入一种评价领域相关性的打分方法,迭到了较好的准确率。本文详细地描述了上述算法并通过详细的实验验证了算法的效率和效果。     

MULTICRITERIA SORTING METHODOLOGY: APPLICATION TO FINANCIAL DECISION PROBLEMS

Abstract

The primary objective in the sorting approach is to assign a set of alternatives into predefined classes. This type of problem is often encountered in many real world decision problems. During the last two decades several new approaches have been proposed to overcome the shortcomings of traditional statistical and econometric techniques. This paper focuses on the multicriteria decision aid (MCDA) approach; it briefly reviews the main MCDA sorting techniques, and presents the multigroup hierarchical discrimination method. This new MCDA sorting technique is applied to the portfolio selection problem. A comparison with discriminant analysis is also performed. Furthermore, the efficiency of the proposal approach can be easily improved for solving large-scale problems in a multiprocessing environment.     

蚂蚁算法在民航飞机排班问题中的应用

蚂蚁算法是一种比较新的组合优化算法,在很多问题的求解中取得了成效。该文将蚂蚁算法引入了民航飞机排班问题的求解过程,并举例说明了蚂蚁算法在飞机排班问题中的可行性,为民航飞机排班问题的解决提出了新的思路。     

融合AdaBoost和启发式特征搜索的人脸性别分类

提出一种基于AdaBoost的人脸性别分类方法,从一张低分辨率灰度人脸图像中辨认出一个人的性别。将启发式搜索算法融于AdaBoost算法框架中,从而发现新的可用于更好分类的特征。利用该方法进行人脸性别分类方面的实验,当使用少于500个像素比较时,正确识别率达到了93%以上,这与迄今已公布的最佳的分类器支持向量机（SVM）的正确识别率相当,但速度却快得多。     

噪声图象中提取边界的随机启发式搜索方法

在噪声图象中如何有效地提取边界是图象分析领域中的难点。启发式搜索的方法常常用于提取边界,但是,这种方法由于采用固定的起始点、固定的引导度量以及对图象仅进行一次性搜索,对噪声往往很敏感,为此提出了一种随机启发式搜索算法,该方法随机地选取起始点,并依照引导度量的概率反复地进行随机搜索获得各种可能的边界轨迹,然后进行各搜索轨迹的积累自增强,最后根据自增强积累统计结果获得边界。大量的实验结果证明,在噪声图象中,随机启发式搜索方法可以在提取出有意义边界的同时有效地抑制噪声。