进化多目标优化设计满意解的模糊决策

文章提出了一种进化多目标优化满意解的模糊决策方法。首先,根据各个子目标满意度对所有pareto最优解的性能做出模糊评价,并在此基础上将整个pareto解集划分为若干个具有不同性能特征的类;然后根据决策者对目标的模糊偏好,从相应的类中选择最有代表性的个体作为最终的满意解。最后以两杆桁架多目标优化问题为例,说明了该方法的应用。     

一种通过量化分析实现非功能需求的方法

提供了一种面向过程非功能需求处理方法，通过量化需求领域信息实现；对非功能需求目标进行分析得到目标优化树，在目标优化树中加入领域信息产生设计分析图，然后对图中领域信息结点进行量化赋以特定的量值，权衡各量值来选择合适的解决方案，还提供了简便的方法来支持系统升级。     

一种通过量化分析实现非功能需求的方法

提供了一种面向过程非功能需求处理方法，通过量化需求领域信息实现；对非功能需求目标进行分析得到目标优化树，在目标优化树中加入领域信息产生设计分析图，然后对图中领域信息结点进行量化赋以特定的量值，权衡各量值来选择合适的解决方案，还提供了简便的方法来支持系统升级。     

基于满意大规模含整变量多目标决策方法

针对具有块角结构的大规模含整变量多目标决策问题，提出一种基于目标满意度的交互式分解协调方法。定义了若干概念，给出了若干定理，介绍了交互式决策方法的具体步骤，并对其软件实现作了说明。     

虚拟环境中的虚拟目标摄像机实现方法

在漫游虚拟环境时,经常需要保持将视点固定在某个目标上,以便于对目标进行跟踪观察,实现这个视点变换功能的摄像机称为虚拟目标摄像机(VTC).可以使用面向对象技术,并结合OPENGL实现一类虚拟目标摄像机,完成对虚拟目标的跟踪,并可在此基础上实现任意视点变换,提供对目标的不同距离和任意角度观察.文章分析了目标摄像机的基本原理,并给出了一种软件实现.最终的实验结果表明,这种方法可以有效地完成目标跟踪,并可做到视点的实时变换,具有较好的可移植性,适用于不同的三维虚拟环境漫游.     

地面移动目标跟踪报警系统设计与实现

为实现对汽车、行人等地面移动目标的安全保护,以全球卫星定位技术为基础,结合GSM无线通信技术、FPGA技术设计了一类地面移动目标跟踪报警系统;全文介绍了地面移动目标跟踪报警系统的结构、功能及工作原理,对基于FPGA的8051软核进行了设计和实现,在此基础上,实现了GPS定位信息的解析,并结合GSM移动通信技术,实现了对地面移动目标的远程跟踪和紧急情况下的报警功能;经组网测试,该跟踪报警系统各部分运行良好,具有稳定的数据采集和传输质量,具有较高的实际应用价值.     

基于多目标满意优化的计算机通信网流量和容量分配

将满意优化理论应用到计算机通信网中，提出了基于多目标满意优化的计算机通信网优化设计方法，通过设计满意度函数，构造出计算机通信网的满意优化数学模型，并用改进遗传算法实现计算机通信网的容量和流量分配的满意优化设计。仿真结果显示，所设计的网络具有更满意的综合性能指标，证实了该方法的有效性和实用性。     

基于满意度大规模含整变量多目标决策方法

针对具有块角结构的大规模含整变量多目标决策问题，提出一种基于目标满意度的交互式分解协调方法。定义了若干概念，给出了若干定理，介绍了交互式决策方法的具体步骤，并对其软件实现作了说明。     

基于随机森林与变邻域下降的车辆合乘求解

为了最大化用户满意度，长期车辆合乘问题（LTCPP）被建模为多目标优化问题。然后，根据历史合乘数据以及用户满意度信息，使用随机森林算法计算每个指标对用户满意度的重要性影响，并作为对应优化目标的权重，以避免人为设定权重因子对优化结果的影响。提出了一种求解LTCPP的变邻域下降（VND）算法，通过顺序地在多个邻域内搜索得到问题的最优解。实验结果表明，结合随机森林和VND算法能为LTCPP提供高质量的解决方案，且具有很高的时间效率。     

基于扩展UML的作战信息需求描述方法研究

在广泛研究需求描述方法的基础上,提出了一种基于扩展UML的作战信息需求描述方法。该方法通过作战目标的静态和动态描述,满足了战前和作战过程中的敌情信息需求。通过构建作战功能需求体系,对我方作战单元信息需求进行定性分析和定量描述。最后,与其他信息需求描述方法进行了对比。基于扩展UML的信息需求描述方法对于消除战场“迷雾”,增强战场透明性,实现作战指挥、控制、决策的扁平化、自动化具有重要意义。     

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.     

Comparison and evaluation of goal-oriented satisfaction analysis techniques

Goal-oriented requirements engineering (GORE) has been introduced as a means of modeling and understanding the motivations for system requirements. Using models to make goals explicit helps to avoid system failures due to implementing the wrong requirements or ignoring certain stakeholder needs. These models are unique when compared to other models used in system analysis in that their structure naturally lends itself to an analysis of goal satisfaction. Existing work claims that analysis using goal models can facilitate decision making over functional or design alternatives, using criteria in the model. Many different approaches to the analysis of goal-oriented requirements models have been proposed, including several procedures that analyze the satisfaction or denial of goals. These procedures make different choices in their interpretation of the goal model syntax, the methods to resolve conflicting or partial evidence, and in the way they represent satisfaction. This work uses three available tools implementing seven similar goal satisfaction analysis procedures to analyze three sample goal models. Results are reported and compared. The purpose of this comparison is to understand the ways in which procedural design choices affect analysis results, and how differences in analysis results could lead to different recommendations over alternatives in the model. Our comparison shows that different satisfaction analysis techniques for goal models can produce variable results, depending on the structure of the model. Comparison findings lead us to recommend the use of satisfaction analysis techniques for goal models as only heuristics for decision making. Our results emphasize investigation into the benefits of satisfaction analysis beyond decision making, namely improving model quality, increasing domain knowledge, and facilitating communication.     

基于监视的服务质量评估方法

在面向服务的体系结构(Service Oriented Architecture,SOA)中,消费者的目标是发现和使用高质量的服务。随着Web服务数量的不断增加,为用户推荐和选择最满足需求的Web服务已经成为服务计算领域最重要的挑战之一。在SOA中,传统的质量评估方法偏重于消费者获得更高的性能,未充分考虑消费者的个性化需求,这样,并不一定让消费者获得完全满足其需求的服务。因此,在服务选择和推荐过程中不仅需要考虑服务的功能性需求,还需要考虑服务的非功能性需求。而且,服务质量的改变只能在服务运行过程中才能被发现。为了解决上述问题,本文提出一种基于监视的服务质量评估方法,充分考虑服务的功能性和非功能性属性,建立统一模型,根据监视数据动态评估服务质量,为动态环境下选择和推荐服务提供依据。本文最后描述了该方法的实现框架。     

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.     

Planning in Multi-agent Environment Using Strips Representation and Non-cooperative Equilibrium Strategy

In multi-agent (multi-robot) environment each agent tries to achieve its own goals leading usually to goals conflict. However, there exists a group of problems with conflicting goals, satisfaction of which is possible simultaneously. Such problems can be modelled as a STRIPS system (for instance Block World environment). If STRIPS planning problem is invertible than it is possible to apply planning under uncertainty methodologies to solve inverted problem and then find a plan that solves multi-agent problem. In the paper, a multi-agent Block World environment is presented as an invertible STRIPS problem. Two cases are considered: when goals conflict and do not conflict. A necessary condition of plan existence is formulated. In the case when goals conflict and agents have different goal preferences we show that it is possible to use non-cooperative equilibrium strategy for modification of the plan found previously. This modification guarantees the best solution (in the sense of non-cooperative equilibrium) for all agents in some cases.     

Querying Incomplete Information in Semistructured Data

Semistructured data occur in situations where information lacks a homogeneous structure and is incomplete. Yet, up to now the incompleteness of information has not been reflected by special features of query languages. Our goal is to investigate the principles of queries that allow for incomplete answers. We do not present, however, a concrete query language. Queries over classical structured data models contain a number of variables and constraints on these variables. An answer is a binding of the variables by elements of the database such that the constraints are satisfied. In the present paper, we loosen this concept in so far as we allow also answers that are partial; that is, not all variables in the query are bound by such an answer. Partial answers make it necessary to refine the model of query evaluation. The first modification relates to the satisfaction of constraints: in some circumstances we consider constraints involving unbound variables as satisfied. Second, in order to prevent a proliferation of answers, we only accept answers that are maximal in the sense that there are no assignments that bind more variables and satisfy the constraints of the query. Our model of query evaluation consists of two phases, a search phase and a filter phase. Semistructured databases are essentially labeled directed graphs. In the search phase, we use a query graph containing variables to match a maximal portion of the database graph. We investigate three different semantics for query graphs, which give rise to three variants of matching. For each variant, we provide algorithms and complexity results. In the filter phase, the maximal matchings resulting from the search phase are subjected to constraints, which may be weak or strong. Strong constraints require all their variables to be bound, while weak constraints do not. We describe a polynomial algorithm for evaluating a special type of queries with filter constraints, and assess the complexity of evaluating other queries for several kinds of constraints. In the final part, we investigate the containment problem for queries consisting only of search constraints under the different semantics.     

Hybrid assessment method for software engineering decisions

During software development, many decisions need to be made to guarantee the satisfaction of the stakeholders' requirements and goals. The full satisfaction of all of these requirements and goals may not be possible, requiring decisions over conflicting human interests as well as technological alternatives, with an impact on the quality and cost of the final solution. This work aims at assessing the suitability of multi-criteria decision making (MCDM) methods to support software engineers' decisions. To fulfil this aim, a HAM (Hybrid Assessment Method) is proposed, which gives its user the ability to perceive the influence different decisions may have on the final result. HAM is a simple and efficient method that combines one single pairwise comparison decision matrix (to determine the weights of criteria) with one classical weighted decision matrix (to prioritize the alternatives). To avoid consistency problems regarding the scale and the prioritization method, HAM uses a geometric scale for assessing the criteria and the geometric mean for determining the alternative ratings.     

A CSP model for simple non-reversible and parallel repair plans

This work presents a constraint satisfaction problem (CSP) model for the planning and scheduling of disassembly and assembly tasks when repairing or substituting faulty parts. The problem involves not only the ordering of assembly and disassembly tasks, but also the selection of them from a set of alternatives. The goal of the plan is the minimization of the total repairing time, and the model considers, apart from the durations and resources used for the assembly and disassembly tasks, the necessary delays due to the change of configuration in the machines, and to the transportation of intermediate subassemblies between different machines. The problem considers that sub-assemblies that do not contain the faulty part are nor further disassembled, but allows non-reversible and parallel repair plans. The set of all feasible repair plans are represented by an extended And/Or graph. This extended representation embodies all of the constraints of the problem, such as temporal and resource constraints and those related to the selection of tasks for obtaining a correct plan.     

Aggregated Fuzzy Answer Set Programming

Fuzzy Answer Set Programming (FASP) is an extension of answer set programming (ASP), based on fuzzy logic. It allows to encode continuous optimization problems in the same concise manner as ASP allows to model combinatorial problems. As a result of its inherent continuity, rules in FASP may be satisfied or violated to certain degrees. Rather than insisting that all rules are fully satisfied, we may only require that they are satisfied partially, to the best extent possible. However, most approaches that feature partial rule satisfaction limit themselves to attaching predefined weights to rules, which is not sufficiently flexible for most real-life applications. In this paper, we develop an alternative, based on aggregator functions that specify which (combination of) rules are most important to satisfy. We extend upon previous work by allowing aggregator expressions to define partially ordered preferences, and by the use of a fixpoint semantics.