DCSP和DCOP求解研究进展

分布式约束满足问题（DCSP）和分布式约束最优问题（DCOP）的研究是分布式人工智能领域的基础性工作。本文首先介绍了卿和DCOP的形式化描述及对实际应用问题的建模方法。在DCSP和DCOP的求解中,通常对问题要进行限制和要求,同时要满足分布性、异步性、局部性、完备性的原则。异步回溯（ABT）、异步弱承诺搜索（AWC）和分布式逃逸（DB）算法是求解DCSP的有代表性的算法;DCSP算法对DCOP求解产生了影响,但由DCSP一般化到DCOP的算法,仅适用于解决部分特定的问题,DCOP的最优、异步算法有异步分布式约束最优算法（A—dopt）和最优异步部分交叉算法（OptAPO）。本文讨论了上述算法的性能。相关的研究工作在多局部变量的处理、超约束DCSP、算法性能度量、通信的保密等方面进行了扩充,在对问题本身的研究、建模方法学、算法、与其他方法的结合以及拓展应用领域等方面仍有许多问题需要进一步研究。     

基于DCSP的煤矿应急救援资源调配方法

在大规模群体突发事件发生后,如何实时及有效地调配资源,是保障应急救援快速实施的关键。以煤矿应急救援为背景,探讨合适的资源调配方法。分布式约束满足问题(D(',SP-Distributed Constraint Satisfaction Problem)擅于表示及求解分布式环境下以协作性为主的问题,是一种解决具有信息分布、需求随环境动态变化等特点的资源调配问题的有效方法,而煤矿应急救援问题正好具有这样的特征。因此,采用DCSP方法来解决煤矿应急救援中的资源调配问题,抽取并构建了煤矿应急救援资源调配的模型,讨论了Agent模型和约束模型的定义,改进了MAWS(MAWS-Multiple Asynchronous Weak-commitment Search)算法。经实验验证,采用DCSP方法可在事故发生后的较短时间内做出有效的资源调配决策,减少资源送达到事故点的时间,为应急救援争取了大量救援时间,从而减少了煤矿事故发生后的人员伤亡和经济损失。     

A dynamic constraint satisfaction approach for configuring structural products under mass customization

Configuring structured products poses new challenges to the solving technologies for product configuration. This paper presents a novel and direct approach to encoding configuration models into the Dynamic Constraint Satisfaction Problems (DCSP). In the presented approach, components are encoded as DCSP variables while structural relationships are represented as DCSP activity constraints. Furthermore, the configuration constraints such as the requisition and exclusion constraints are treated as DCSP compatibility constraints, which allow a low-level component to join in the solving process only after its high-level component is selected in the configuration. The presented method allows a more compact encoding representation, compared to CSP and generative CSP. Experimental study shows that the presented DCSP encoding approach makes a significant improvement in the performance of product configuration.     

Multi-variable distributed backtracking with sessions

The Constraint Satisfaction Problem (CSP) formalism is used to represent many combinatorial decision problems instances simply and efficiently. However, many such problems cannot be solved on a single, centralized computer for various reasons (e.g., their excessive size or privacy). The Distributed CSP (DisCSP) extends the CSP model to allow such combinatorial decision problems to be modelled and handled. In this paper, we propose a complete DisCSP-solving algorithm, called Distributed Backtracking with Sessions (DBS), which can solve DisCSP so that each agent encapsulates a whole “complex” problem with many variables and constraints. We prove that the algorithm is sound and complete, and generates promising experimental results.     

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).     

炼钢机器故障下炼钢-连铸调度DCSP建模与算法

针对机器故障下的炼钢-连铸动态调度问题,基于动态约束满足技术开发了能够灵活反映各种动态因素的建模机制。从变量、值域和约束三个角度将生产过程中的机器故障的影响映射为约束满足模型的动态变化;提出了重调度前后调度方案在时间安排和机器指派上的一致性度量方法,以满足不同炉次对时间和机器一致性的不同要求;将机器故障扰动按影响程度分为3个层级,建立了故障扰动与约束满足调度模型间的映射关系。根据炼钢、精炼阶段的机器故障扰动程度,制定不同的求解策略,并为机器指派变量赋值;基于约束传播技术,通过调整开工时间和柔性加工时间分步消解时间约束冲突。仿真实验表明,提出的模型和算法是可行和有效的。     

基于多AGENT的虚拟企业伙伴选择系统

伙伴选择是虚拟企业建立过程中的核心问题。在基于多agent的虚拟企业体系结构基础上,通过本体驱动的虚拟企业目标体系描述与分解,将虚拟企业伙伴选择问题表示为分布式约束满足和优化问题,并进一步以agent谈判的方式来求解该问题,最终得到优化的伙伴选择方案。本体驱动的虚拟企业目标描述与分解方法,为不同企业之间的谈判提供了通用词汇;用分布式约束满足和优化的方法对问题进行形式化描述,可以为企业之间的谈判提供论域和决策支持。分布式约束满足和优化方法的应用,为解决虚拟企业伙伴选择问题提供了一种新思路。     

meSAT: multiple encodings of CSP to SAT

One approach for solving Constraint Satisfaction Problems (CSP) (and related Constraint Optimization Problems (COP)) involving integer and Boolean variables is reduction to propositional satisfiability problem (SAT). A number of encodings (e.g., direct, log, support, order) for this purpose exist as well as specific encodings for some constraints that are often encountered (e.g., cardinality constraints, global constraints). However, there is no single encoding that performs well on all classes of problems and there is a need for a system that supports multiple encodings. We present a system that translates specifications of finite linear CSP problems into SAT instances using several well-known encodings, and their combinations. We also present a methodology for selecting a suitable encoding based on simple syntactic features of the input CSP instance. Thorough evaluation has been performed on large publicly available corpora and our encoding selection method improves upon the efficiency of existing encodings and state-of-the-art tools used in comparison.     

Integrating domain and constraint privacy reasoning in the distributed stochastic algorithm with breakouts

Privacy has traditionally been a major motivation of distributed problem solving. One popular approach to enable privacy in distributed environments is to implement complex cryptographic protocols. In this paper, we propose a different, orthogonal approach, which is to control the quality and the quantity of publicized data. We consider the Open Constraint Programming model and focus on algorithms that solve Distributed Constraint Optimization Problems (DCOPs) using a local search approach. Two such popular algorithms exist to find good solutions to DCOP: DSA and GDBA. In this paper, we propose DSAB, a new algorithm that merges ideas from both algorithms to allow extensive handling of constraint privacy. We also study how algorithms behave when solving Utilitarian DCOPs, where utilitarian agents want to reach an agreement while reducing the privacy loss. We experimentally study how the utilitarian approach impacts the quality of the solution and of publicized data.

     

Weight constraint programs with evaluable functions

In the current practice of Answer Set Programming (ASP), evaluable functions are represented as special kinds of relations. This often makes the resulting program unnecessarily large when instantiated over a large domain. The extra constraints needed to enforce the relation as a function also make the logic program less transparent. In this paper, we consider adding evaluable functions to answer set logic programs. The class of logic programs that we consider here is that of weight constraint programs, which are widely used in ASP. We propose an answer set semantics to these extended weight constraint programs and define loop completion to characterize the semantics. Computationally, we provide a translation from loop completions of these programs to instances of the Constraint Satisfaction Problem (CSP) and use the off-the-shelf CSP solvers to compute the answer sets of these programs. A main advantage of this approach is that global constraints implemented in such CSP solvers become available to ASP. The approach also provides a new encoding for CSP problems in the style of weight constraint programs. We have implemented a prototype system based on these results, and our experiments show that this prototype system competes well with the state-of-the-art ASP solvers. In addition, we illustrate the utilities of global constraints in the ASP context.     

Node and Link Allocation in Network Virtualization Based on Distributed Constraint Optimization

Virtual Networks (VNs) offer a flexible and economic approach to deploy customer suited networks. However, defining how resources of a physical network are used to support VNs requirements is a NP-hard problem. For this reason, heuristics have been used on mapping of virtual networks. Although heuristics do not ensure the optimal solution, they implement fast solutions and showed satisfactory results. This work presents a modeling of the node and link allocation problem using Distributed Constraint Optimization Problem (DCOP) with factor graphs, which is a formalism widely used in real distributed optimization problems. In our approach, we use the max-sum algorithm to solve the DCOP. Correctness criteria for this approach are discussed and verifications are conducted through model checking.     

Portfolio approaches for constraint optimization problems

Within the Constraint Satisfaction Problems (CSP) context, a methodology that has proven to be particularly performant consists of using a portfolio of different constraint solvers. Nevertheless, comparatively few studies and investigations have been done in the world of Constraint Optimization Problems (COP). In this work, we provide a generalization to COP as well as an empirical evaluation of different state of the art existing CSP portfolio approaches properly adapted to deal with COP. The results obtained by measuring several evaluation metrics confirm the effectiveness of portfolios even in the optimization field, and could give rise to some interesting future research.     

DCOP FOR SMART HOMES: A CASE STUDY

The aim of this article is to bring forth the issue of integrating the services provided by intelligent artifacts in Ambient Intelligence applications. Specifically, we propose a Distributed Constraint Optimization procedure for achieving a functional integration of intelligent artifacts in a smart home. To this end, we employ Adopt-N , a state-of-the-art algorithm for solving Distributed Constraint Optimization Problems (DCOP). This article attempts to state the smart home coordination problem in general terms, and provides the details of a DCOP-based approach by describing a case study taken from the RoboCare project. More specifically, we show how (1) DCOP is a convenient metaphor for casting smart home coordination problems, and (2) the specific features which distinguish Adopt-N from other algorithms for DCOP represent a strong asset in the smart home domain.     

一个基于最小冲突修补的动态约束满足求解算法

约束满足问题是人工智能中一个重要的研究方向,近年来,对动态变化的约束满足问题的研究逐渐成为该领域的热点.在目前该领域最流行的LC算法基础上,引入禁忌搜索策略,提出了一个基于最小冲突修补的算法Tabu_LC.算法在每次冲突调整时将所有冲突变量看成一个整体,并采用分支定界搜索策略求解冲突变量组成的子问题,极大地提高了求解效率.同时,在约束求解系统"明月1.0"架构下给出了算法的具体实现,并针对大量随机问题进行了对比实验.结果表明,Tabu_LC算法在求解效率和解的质量上都明显优于LC算法.     

Applying constraint satisfaction approach to solve product configuration problems with cardinality-based configuration rules

In this paper, the product configuration problems that are characterized by cardinality-based configuration rules are dealt with. Novel configuration rules including FI and EI rules are presented to clarify the semantics of inclusion rules when cardinalities and hierarchies of products are encountered. Then, a configuration graph is proposed to visualize structural rules and configuration rules in product configuration problem. An encoding approach is elaborated to transform the configuration graph as a CSP (Constraint Satisfaction Problem). As a consequence, existing CSP solver, i.e. JCL (Java Constraint Library), is employed to implement the configuration system for product configuration problem with cardinality-related configuration rules. A case study of a bus configuration is used throughout this paper to illustrate the effectiveness of the presented approach.     

XML数据查询的CSP实现和算法改进

XML犤1犦正在迅速成为WWW上采用的信息交换、表示和存储手段之一,该文简单介绍了基于OEM数据模型犤2犦的离散XML数据模式概念和在此思想上发展起来的基于模式匹配的数据查询概念;通过将模式匹配转换到约束满足问题(CSP)来具体求解匹配实例,从而使WWW基于内容的查询转变为更为精确的基于结构的查询;接下来,从理论上证明了这种模式匹配到CSP转换的可行性;最后,讨论了求解CSP的算法的改进问题。     

Reasoning with Numeric and Symbolic Time Information

Representing and reasoning about time is fundamental in many applications of Artificial Intelligence as well as of other disciplines in computer science, such as scheduling, planning, computational linguistics, database design and molecular biology. The development of a domain-independent temporal reasoning system is then practically important. An important issue when designing such systems is the efficient handling of qualitative and metric time information. We have developed a temporal model, TemPro, based on the Allen interval algebra, to express and manage such information in terms of qualitative and quantitative temporal constraints. TemPro translates an application involving temporal information into a Constraint Satisfaction Problem (CSP). Constraint satisfaction techniques are then used to manage the different time information by solving the CSP. In order for the system to deal with real time applications or those applications where it is impossible or impractical to solve these problems completely, we have studied different methods capable of trading search time for solution quality when solving the temporal CSP. These methods are exact and approximation algorithms based respectively on constraint satisfaction techniques and local search. Experimental tests were performed on randomly generated temporal constraint problems as well as on scheduling problems in order to compare and evaluate the performance of the different methods we propose. The results demonstrate the efficiency of the MCRW approximation method to deal with under constrained and middle constrained problems while Tabu Search and SDRW are the methods of choice for over constrained problems.     

A unified framework for consistency check

Constraint Satisfaction Problem (CSP) involves finding values for variables to satisfy a set of constraints. Consistency check is the key technique in solving this class of problems. Past research has developed many algorithms for such a purpose, e.g., node consistency, are consistency, generalized node and arc consistency, specific methods for checking specific constraints, etc. In this article, an attempt is made to unify these algorithms into a common framework. This framework consists of two parts. the first part is a generic consistency check algorithm, which allows and encourages each individual constraint to be checked by its specific consistency methods. Such an approach provides a direct way of practical implementation of the CSP model for real problem-solving. the second part is a general schema for describing the handling of each type of constraint. the schema characterizes various issues of constraint handling in constraint satisfaction, and provides a common language for expressing, discussing, and exchanging constraint handling techniques. © 1995 John Wiley & Sons, Inc.     

Multi-agent oriented constraint satisfaction

This paper presents a multi-agent oriented method for solving CSPs (Constraint Satisfaction Problems). In this method, distributed agents represent variables and a two-dimensional grid-like environment in which the agents inhabit corresponds to the domains of the variables. Thus, the positions of the agents in such an environment constitute the solution to a CSP. In order to reach a solution state, the agents will rely on predefined local reactive behaviors; namely, better-move, least-move, and random-move. While presenting the formalisms and algorithm, we will analyze the correctness and complexity of the algorithm, and demonstrate the proposed method with two benchmark CSPs, i.e., n-queen problems and coloring problems. In order to further determine the effectiveness of different reactive behaviors, we will examine the performance of this method in deriving solutions based on behavior prioritization and different selection probabilities.