基于有向图的二维约束求解算法研究

针对过约束、几何完全定义状态判定和约束求解效率等问题,提出了基于约束图,利用自由度理论和约束冲突机制,通过反向约束方向平衡约束,进而通过排序进行约束求解的算法。算法采用约束图记录约束和几何的关系;通过约束平衡的方法进行过约束和几何完全定义的判定;采用排序求解方法,将庞大计算问题转化为一组相对简单的计算问题。算法已得到初步应用,对过约束和几何完全定义状态的判定有明显的效果,而且提高了约束求解效率。     

An agent-based approach to solve dynamic meeting scheduling problems with preferences

Multi-agent systems are widely used to address large-scale distributed combinatorial applications in the real world. One such application is meeting scheduling (MS), which is defined by a variety of features. The MS problem is naturally distributed and especially subject to many alterations. In addition, this problem is characterized by the presence of users’ preferences that turn it into a search for an optimal rather than a feasible solution. However, in real-world applications users usually have conflicting preferences, which makes the solving process an NP-hard problem. Most research efforts in the literature, adopting agent-based technologies, tackle the MS problem as a static problem. They often share some common properties: allowing the relaxation of any user's time restriction, not dealing with achieving any level of consistency among meetings to enhance the efficiency of the solving process, not tackling the consequences of the dynamic environment, and especially not addressing the real difficulty of distributed systems which is the complexity of message passing operations.In an attempt to facilitate and streamline the process of scheduling meetings in any organization, the main contribution of this work is a new scalable agent-based approach for any dynamic MS problem (that we called MSRAC, for Meeting Scheduling with Reinforcement of Arc Consistency). In this approach we authorize only the relaxation of users’ preferences while maintaining arc-consistency on the problem. The underlying protocol can efficiently reach the optimal solution (satisfying some predefined optimality criteria) whenever possible, using only minimum localized asynchronous communications. This purpose is achieved with minimal message passing while trying to preserve at most the privacy of involved users. Detailed experimental results on randomly generated MS problems show that MSRAC is scalable and it leads to speed up over other approaches, especially for large problems with strong constraints.     

Toward automated feature model configuration with optimizing non-functional requirements

ContextA software product line is a family of software systems that share some common features but also have significant variabilities. A feature model is a variability modeling artifact, which represents differences among software products with respect to the variability relationships among their features. Having a feature model along with a reference model developed in the domain engineering lifecycle, a concrete product of the family is derived by binding the variation points in the feature model (called configuration process) and by instantiating the reference model.ObjectiveIn this work we address the feature model configuration problem and propose a framework to automatically select suitable features that satisfy both the functional and non-functional preferences and constraints of stakeholders. Additionally, interdependencies between various non-functional properties are taken into account in the framework.MethodThe proposed framework combines Analytical Hierarchy Process (AHP) and Fuzzy Cognitive Maps (FCM) to compute the non-functional properties weights based on stakeholders’ preferences and interdependencies between non-functional properties. Afterwards, Hierarchical Task Network (HTN) planning is applied to find the optimal feature model configuration.ResultOur approach improves state-of-art of feature model configuration by considering positive or negative impacts of the features on non-functional properties, the stakeholders’ preferences, and non-functional interdependencies. The approach presented in this paper extends earlier work presented in [1] from several distinct perspectives including mechanisms handling interdependencies between non-functional properties, proposing a novel tooling architecture, and offering visualization and interaction techniques for representing functional and non-functional aspects of feature models.Conclusionour experiments show the scalability of our configuration approach when considering both functional and non-functional requirements of stakeholders.     

Solving Hierarchical Constraints over Finite Domains with Local Search

Many real world problems have requirements and constraints which conflict with each other. One approach for dealing with such over-constrained problems is with constraint hierarchies. In the constraint hierarchy framework, constraints are classified into ranks, and appropriate solutions are selected using a comparator which takes into account the constraints and their ranks. In this paper, we present a local search solution to solving hierarchical constraint problems over finite domains (HCPs). This is an extension of local search for over-constrained integer programs WSAT(OIP) to constraint hierarchies and general finite domain constraints. The motivation for this work arose from solving large airport gate allocation problems. We show how gate allocation problems can be formulated as HCPs using typical gate allocation constraints. Using the gate allocation benchmarks, we investigate how constraint heirarchy selection strategies and the problem formulation using two models: a 0–1 linear constraint hierarchy model and a nonlinear finite domain constraint hierarchy model.     

General Properties and Termination Conditions for Soft Constraint Propagation

Soft constraints based on semirings are a generalization of classical constraints, where tuples of variables' values in each soft constraint are associated to elements from an algebraic structure called semiring. This framework is able to express, for example, fuzzy, classical, weighted, valued and over-constrained constraint problems.Classical constraint propagation has been extended and adapted to soft constraints by defining a schema for soft constraint propagation [8]. On the other hand, in [1–3] it has been proven that most of the well known constraint propagation algorithms for classical constraints can be cast within a single schema.In this paper we combine these two schemas and we provide a more general framework where the schema of [3] can be used for soft constraints. In doing so, we generalize the concept of soft constraint propagation, and we provide new sufficient and independent conditions for its termination.     

Smart Parking Reservation System Based on Distributed Multicriteria Approach

In metropolitan area, finding a parking space is a difficult task for drivers especially in rush hours. This causes waste of time and fuel and results in traffic congestion. We propose a smart parking system based on multi-agent approach to provide a real-time decision-aid for drivers by handling their preferences. The system ensures an online space allocation based on real-time information by optimizing drivers’ preferences with respect to operational constraints. The online allocation problem is considered as a multicriteria decision-aid problem for which we present a mathematical formulation: multicriteria parking reservation problem. The solution is an optimal compromise from the set of efficient solutions which is determined by means of a multicriteria ranking method ELECTRE III. An update of resource allocation is performed to avoid reservation conflict and to ensure constraints satisfaction. After the reservation process, the driver is assisted via a guidance module to reach the reserved place through the shortest path. Simulation results show the wide applicability of the approach in real cases.     

User preference mining techniques for personalized applications

Advanced personalized e-applications require comprehensive knowledge about their users’ likes and dislikes in order to provide individual product recommendations, personal customer advice, and custom-tailored product offers. In our approach we model such preferences as strict partial orders with “A is better than B” semantics, which has been proven to be very suitable in various e-applications. In this paper we present preference mining techniques for detecting strict partial order preferences in user log data. Real-life e-applications like online shops or financial services usually have large log data sets containing the transactions of their customers. Since the preference miner uses sophisticated SQL operations to execute all data intensive operations on database layer, our algorithms scale well even for such large log data sets. With preference mining personalized e-applications can gain valuable knowledge about their customers’ preferences, which can be applied for personalized product recommendations, individual customer service, or one-to-one marketing.     

Flexible multibody modelling for exact constraint design of compliant mechanisms

In high precision equipment, the use of compliant mechanisms is favourable as elastic joints offer the advantages of low friction and no backlash. If the constraints in a compliant mechanism are not carefully dealt with, even small misalignments can lead to changes in natural frequencies and stiffnesses. Such unwanted behaviour can be avoided by applying exact constraint design, which implies that the mechanism should have exactly the required degrees of freedom and non-redundant constraints so that the system is kinematically and statically determinate. For this purpose, we propose a kinematic analysis using a finite element based multibody modelling approach. In compliant mechanisms, the system’s degrees of freedom are presented clearly from the analysis of a system in which the deformation modes with a low stiffness are free to deform while the deformation modes with a high stiffness are considered rigid. If the Jacobian matrix associated with the dependent coordinates is not full column or row rank, the system is under-constrained or over-constrained. The rank of this matrix is calculated from a singular value decomposition. For an under-constrained system, any motion in the mechanism that is not accounted for by the current set of degrees of freedom is visualised using data from the right singular matrix. For an over-constrained system, a statically indeterminate stress distribution is derived from the left singular matrix and is used to visualise the over-constraints. The analysis is exemplified for the design of a straight guiding mechanism, where under-constrained and over-constrained conditions are visualised clearly.     

基于QoS预测与约束层次模型的云服务推荐方法研究

如何精确地预测云服务的QoS,灵活地处理用户的QoS约束是实现可靠云服务推荐的关键问题。针对上述问题,提出了一种基于事例推理的云服务QoS动态预测方法,用于预测候选云服务的QoS值,提高云服务质量的可靠性;提出了一种基于约束层次（Constraint Hierarchy,CH）的QoS评价模型,该模型依据QoS约束对用户业务的重要程度,将其分为必须满足的硬约束层和允许被偏离的软约束层,该模型能够有效地解决过约束问题。通过实验验证了提出的云服务推荐方法的可行性和有效性。     

Dynamic decision models for staged software product line configuration

Software product line engineering practices offer desirable characteristics such as rapid product development, reduced time-to-market, and more affordable development costs as a result of systematic representation of the variabilities of a domain of discourse that leads to methodical reuse of software assets. The development lifecycle of a product line consists of two main phases: domain engineering, which deals with the understanding and formally modeling of the target domain, and application engineering that is concerned with the configuration of a product line into one concrete product based on the preferences and requirements of the stakeholders. The work presented in this paper focuses on the application engineering phase and builds both the theoretical and technological tools to assist the stakeholders in (a) understanding the complex interactions of the features of a product line; (b) eliciting the utility of each feature for the stakeholders and hence exposing the stakeholders’ otherwise implicit preferences in a way that they can more easily make decisions; and (c) dynamically building a decision model through interaction with the stakeholders and by considering the structural characteristics of software product line feature models, which will guide the stakeholders through the product configuration process. Initial exploratory empirical experiments that we have performed show that our proposed approach for helping stakeholders understand their feature preferences and its associated staged feature model configuration process is able to positively impact the quality of the end results of the application engineering process within the context of the limited number of participants. In addition, it has been observed that the offered tooling support is able to ease the staged feature model configuration process.     

Hanging cloths and dangling rods: A unified approach to constraints in computer animation

A simple unified approach for dealing with constraints on real-valued parameters in computer simulations and computer animation is described. The main characteristics of the approach are its flexibility (it works for a variety of constraint-types and it can be implemented to handle both under- and over-constrained systems in a stable way) and its ease for distributed computation. The method is based on the idea of genetic algorithms. The mathematical merits of the method for one constraint-type are discussed in some detail and some examples of applications are given.     

Defining tools to address over-constrained geometric problems in Computer Aided Design

This paper proposes a new tool for decision support to address geometric over-constrained problems in Computer Aided Design (CAD). It concerns the declarative modeling of geometrical problems. The core of the coordinate free solver used to solve the Geometric Constraint Satisfaction Problem (GCSP) was developed previously by the authors. This research proposes a methodology based on Michelucci’s witness method to determine whether the structure of the problem is over-constrained. In this case, the authors propose a tool for assisting the designer in solving the over-constrained problem by ensuring the consistency of the specifications. An application of the methodology and tool is presented in an academic example.     

Managing the constraints of boundary spanning in emergency management

Stakeholders tasked with boundary spanning in emergency management are fundamental in facilitating multi-agency coordination. However, there is a scarcity of research investigating the characteristics of emergency management boundary spanners and how they achieve this function in the complex environment of emergency operation centres. An exploratory case study approach was adopted and applied in a strategic-level emergency operations centre. The study used three very different but interrelated qualitative research techniques based upon the Core-Task Analysis framework to categorize the work of stakeholders fulfilling a boundary spanning role in this setting. The data identified that stakeholders performing boundary spanning activities in a strategic-level emergency operations centre face a number of constraints. These can include unfamiliarity with the work domain, its personnel, and structure which can lead to temporal, cultural and information challenges. In order to manage these constraints, boundary spanners working in a strategic-level emergency operations centre need to adopt certain characteristics in order to accomplish their activities. A significant outcome from the data was the necessity to engage in these important undertakings in the pre-response phase in an effort to facilitate successful multi-agency coordination in an actual emergency event.     

A new constructive approach to constraint-based geometric design

In the paper, a new constructive approach to solving geometric constraints in 2-D space is presented. Constraints are employed on lines and points only, but more sophisticated geometric elements like Bézier curves and ellipses can also be constrained by mapping them onto auxiliary lines and points. The algorithm is based on local propagation, but first, the problem is transformed into a form that guarantees success of employing this simple technique. The most important steps are substitution of complex constraints with sets of simpler ones and insertion of redundant constraints by solving triangles and determining sums and differences of adjacent angles. In this way, various well-constrained problems with a few exceptions are solved, over-constrained scenes and input data contradictory to some well-known mathematical theorems are detected, and the algorithm is proved successful in many under-constrained cases as well.     

Solving Interval Constraints by Linearization in Computer-Aided Design

Current parametric CAD systems require geometric parameters to have fixed values. Specifying fixed parameter values implicitly adds rigid constraints on the geometry, which have the potential to introduce conflicts during the design process. This paper presents a soft constraint representation scheme based on nominal interval. Interval geometric parameters capture inexactness of conceptual and embodiment design, uncertainty in detail design, as well as boundary information for design optimization. To accommodate under-constrained and over-constrained design problems, a double-loop Gauss-Seidel method is developed to solve linear constraints. A symbolic preconditioning procedure transforms nonlinear equations to separable form. Inequalities are also transformed and integrated with equalities. Nonlinear constraints can be bounded by piecewise linear enclosures and solved by linear methods iteratively. A sensitivity analysis method that differentiates active and inactive constraints is presented for design refinement.     

Video personalization in heterogeneous and resource-constrained environments

Access to multimedia data and multimedia services is becoming increasingly common in networked mobile environments. In such environments, both the mobile client devices and multimedia servers are typically resource constrained. Moreover, the mobile client population is often heterogeneous in terms of the clients’ preferences with regard to multimedia content, the clients’ quality of service requirements and system-level resource constraints. In order to provide a resource-constrained mobile client with its desired video content, it is often necessary to personalize the requested multimedia content in a manner that satisfies simultaneously the various client-specified content preferences and the system-level resource constraints. Also, in order to simultaneously reduce the client-experienced latency, provide optimal quality of service to the clients and ensure efficient utilization of server and network resources, it is necessary to perform client request aggregation on the server end. To this end, a video personalization strategy is proposed to provide mobile, resource-constrained clients with personalized video content that is most relevant to the clients’ requests while satisfying simultaneously multiple client-side system-level resource constraints. A client request aggregation strategy is also proposed to cluster client requests with similar video content preferences and similar client-side resource constraints such that the number of requests the server needs to process and service, and the client-experienced latency are both reduced simultaneously. The primary contributions of the paper are: (1) the formulation and implementation of a Multiple-choice Multi-dimensional Knapsack Problem (MMKP)-based video personalization strategy; and (2) the design and implementation of a client request aggregation strategy based on a multi-stage clustering algorithm. Experimental results comparing the proposed MMKP-based video personalization strategy to existing 0/1 Knapsack Problem (0/1KP)-based and the Fractional Knapsack Problem (FKP)-based video personalization strategies are presented. It is observed that: (1) the proposed MMKP-based personalization strategy includes more relevant video content in response to the client’s request compared to the existing 0/1KP-based and FKP-based personalization strategies; and (2) in contrast to the 0/1KP-based and FKP-based personalization strategies which can satisfy only a single client-side resource constraint at a time, the proposed MMKP-based personalization strategy is capable of satisfying multiple client-side resource constraints simultaneously. Experimental results comparing the client-experienced latency with and without the proposed server-side client request aggregation strategy are also presented. It is shown that the proposed client request aggregation strategy reduces the mean client-experienced latency without significant reduction in the average relevance of the delivered video content and without significant deviation in the client-side resources actually consumed by the delivered video content from the client-specified resource constraints.     

Cooperative Resolution of Over-Constrained Information Requests

In this paper, we present a constraint-based model of cooperative agents for information systems dialogues, with an emphasis on how the agents detect and resolve situations in which the user's information needs have been over-constrained. The constraint-based model of the information agents integrates and extends the AI techniques of constraint satisfaction, solution synthesis and constraint hierarchy, providing an incremental computational mechanism for constructing and maintaining partial parallel solutions. Such a mechanism supports immediate detection of over-constrained situations. In addition, we explore using the knowledge in the solution synthesis network to support different relaxation strategies to support cooperative dialogue behaviors.     

DOC-BRelax: A new multi-agent system to solve Distributed Constraint Optimization Problems

Many problems in multi-agent systems can be described as Distributed Constraint Satisfaction Problems (DCSPs), where the goal is to find a set of assignments to variables that satisfies all constraints among agents. However, when real-life application problems are formalized as DCSPs, they are often over-constrained and have no solution that satisfies all constraints. Moreover, the globalization of the economy and democratization of the Internet, boosted by the huge growth in information and communication technologies, have largely contributed to the expansion of numerous distributed architectures. Thus this paper provides a new distributed management and decision support system suitable to these interdependencies and these complex environments. We present a Distributed Optimization under Constraints Basic Relax (DOC-BRelax) as a new framework for dealing with over-constrained situations. We also present a version of this framework called DOC-MaxRelax and a new algorithm for solving Distributed Maximal Constraint Satisfaction Problems (DMCSPs).     

Intelligent multi-stakeholder environmental management

In any group decision making, stakeholders have different powers, proficiency and also experiences. These power weights are very difficult to obtain, because group managers avoid revealing the relative powers of the stakeholders to prevent more conflict among them. Therefore, in many studies, the different powers have not been well accounted and then equal power weights have been assigned to each stakeholder. This paper will show that considering the powers is necessary and then it introduces a new intelligent approach to obtain consensus based relative power weights. This method is based on the opinions of the stakeholders on the alternatives. A case study of watershed management is used to illustrate the application of the model to a real decision making problem. A suitable aggregation operator is also used to combine the goodness measures, considering the optimism/pessimism view of the group manager. Results indicate that obtaining the stakeholders’ weights and also considering the preferences of the group manager on the risk are essential parts for soft group decision making process, especially in the environmental management problems.