约束满足问题并行弧相容算法

本文给出了约束满足问题网络弧相容的两个并行算法ＰＡＣ－１和ＰＡＣ－２。     

A Glimpse of Constraint Satisfaction

Constraint satisfaction has become an important field in computer science. This technology is embedded in millions of pounds of software used by major companies. Many researchers or software engineers in the industry could have benefited from using constraint technology without realizing it. The aim of this paper is to promote constraint technology by providing readers with a fairly quick introduction to this field. The approach here is to use the well known 8-queens problem to illustrate the basic techniques in constraint satisfaction (without going into great details), and leave interested readers with pointers to further study this field.     

When Ants Attack: Ant Algorithms for Constraint Satisfaction Problems

We describe an ant algorithm for solving constraint problems (Solnon 2002, IEEE Transactions on Evolutionary Computation 6(4): 347–357). We devise a number of variants and carry out experiments. Our preliminary results suggest that the best way to deposit pheromone and the best heuristics for state transitions may differ from current practice     

An Arc-Consistency Algorithm for Dynamic and Distributed Constraint Satisfaction Problems

This paper presents the new DDAC4 algorithm for dynamic arc consistency enforcement in distributed constraint satisfaction problems (CSP). The algorithm is an adaptation of the well-known AC-4 algorithm to system settings where constraints can be added and deleted in concurrent processes. It is the first algorithm for arc-consistency enforcement in this system setting. Arc-consistency is achieved whenever the overall system reaches quiescence after a constraint is added or deleted.     

约束编程与约束满足在产品装配中的应用

约束编程与约束满足问题是近三十年来在人工智能领域发展起来的一个研究方向。产品配置器近十几年来发展起来的一项技术。文中介绍约束编程及约束满足问题在按订单装配型产品配置器中的应用，并通过讨论说明了相对于传统配置方案求解过程，应用约束满足的优越性。     

Message delay and DisCSP search algorithms

Distributed constraint satisfaction problems (DisCSPs) are composed of agents, each holding its own variables, that are connected by constraints to variables of other agents. Due to the distributed nature of the problem, message delay can have unexpected effects on the behavior of distributed search algorithms on DisCSPs. This has been recently shown in experimental studies of asynchronous backtracking algorithms (Bejar et al., Artif. Intell., 161:117–148, 2005; Silaghi and Faltings, Artif. Intell., 161:25–54, 2005). To evaluate the impact of message delay on the run of DisCSP search algorithms, a model for distributed performance measures is presented. The model counts the number of non concurrent constraints checks, to arrive at a solution, as a non concurrent measure of distributed computation. A simpler version measures distributed computation cost by the non-concurrent number of steps of computation. An algorithm for computing these distributed measures of computational effort is described. The realization of the model for measuring performance of distributed search algorithms is a simulator which includes the cost of message delays. Two families of distributed search algorithms on DisCSPs are investigated. Algorithms that run a single search process, and multiple search processes algorithms. The two families of algorithms are described and associated with existing algorithms. The performance of three representative algorithms of these two families is measured on randomly generated instances of DisCSPs with delayed messages. The delay of messages is found to have a strong negative effect on single search process algorithms, whether synchronous or asynchronous. Multi search process algorithms, on the other hand, are affected very lightly by message delay.     

Ultraviolet: A Constraint Satisfaction Algorithm for Interactive Graphics

Ultraviolet is a constraint satisfaction algorithm intended for use in interactive graphical applications. It is capable of solving constraints over arbitrary domains using local propagation, and inequality constraints and simultaneous linear equations over the reals. To support this, Ultraviolet is a hybrid algorithm that allows different subsolvers to be used for different parts of the constraint graph, depending on graph topology and kind of constraints. In addition, Ultraviolet and its subsolvers support plan compilation, producing efficient compiled code that can be evaluated repeatedly to resatisfy a given collection of constraints for different input values.     

Fast Theta-Subsumption with Constraint Satisfaction Algorithms

Relational learning and Inductive Logic Programming (ILP) commonly use as covering test the -subsumption test defined by Plotkin. Based on a reformulation of -subsumption as a binary constraint satisfaction problem, this paper describes a novel -subsumption algorithm named Django,¹ which combines well-known CSP procedures and -subsumption-specific data structures. Django is validated using the stochastic complexity framework developed in CSPs, and imported in ILP by Giordana et Saitta. Principled and extensive experiments within this framework show that Django improves on earlier -subsumption algorithms by several orders of magnitude, and that different procedures are better at different regions of the stochastic complexity landscape. These experiments allow for building a control layer over Django, termed Meta-Django, which determines the best procedures to use depending on the order parameters of the -subsumption problem instance. The performance gains and good scalability of Django and Meta-Django are finally demonstrated on a real-world ILP task (emulating the search for frequent clauses in the mutagenesis domain) though the smaller size of the problems results in smaller gain factors (ranging from 2.5 to 30).     

基于动态约束满足框架的强表达时态规划算法

智能规划已经成为人工智能领域最热门的研究主题之一。近年来,智能规划在现实领域的应用越来越广泛,这对规划器的处理能力和效率提出了很大的挑战。以一类强表达时态规划——基于约束区间规划为研究对象,基于动态约束满足框架设计和实现了一个基于约束区间的规划算法LP-TPOP;对算法的可靠性和完备性进行了证明;最后以一个规划实例演示了算法的运行过程。     

A New Approach for Weighted Constraint Satisfaction

We consider the Weighted Constraint Satisfaction Problem which is an important problem in Artificial Intelligence. Given a set of variables, their domains and a set of constraints between variables, our goal is to obtain an assignment of the variables to domain values such that the weighted sum of satisfied constraints is maximized. In this paper, we present a new approach based on randomized rounding of semidefinite programming relaxation. Besides having provable worst-case bounds for domain sizes 2 and 3, our algorithm is simple and efficient in practice, and produces better solutions than some other polynomial-time algorithms such as greedy and randomized local search.     

Solution Techniques for Constraint Satisfaction Problems: Advanced Approaches

Conventional techniques for the constraint satisfaction problem (CSP)have had considerable success in their applications. However,there are many areas in which the performance of the basic approachesmay be improved. These include heuristic ordering of certain tasksperformed by the CSP solver, hybrids which combine compatible solutiontechniques and graph based methods which exploit the structure of theconstraint graph representation of a CSP. Also, conventionalconstraint satisfaction techniques only address problems with hardconstraints (i.e. each of which are completely satisfied or completelyviolated, and all of which must be satisfied by a validsolution). Many real applications require a more flexible approachwhich relaxes somewhat these rigid requirements. To address theseissues various approaches have been developed. This paper attempts asystematic review of them.     

A Context for Constraint Satisfaction Problem Formulation Selection

Much research effort has been applied to finding effective ways for solving constraint satisfaction problems. However, the most fundamental aspect of constraint satisfaction problem solving, problem formulation, has received much less attention. This is important because the selection of an appropriate formulation can have dramatic effects on the efficiency of any constraint satisfaction problem solving algorithm.In this paper, we address the issue of problem formulation. We identify the heuristic nature of generating a good formulation and we propose a context for this process. Our work presents the research community with a focus for the many elements which affect problem formulation and this is illustrated with the example adding redundant constraints. It also provides a significant step towards the goal of automatic selection of problem formulations.     

改进求解约束满足问题粗粒度弧相容算法

约束满足问题在人工智能领域有着广泛的应用.研究了约束满足问题的粗粒度维持弧相容求解算法,发现在求解过程中,对于指向已赋值变量的弧存在无效的修正检查,证明了这类修正检查是冗余的.提出一种方法避免这类冗余的修正检查,给出改进后的粗粒度弧相容算法的基本框架AC3_frame_ARR,该改进框架可用于改进所有粗粒度弧相容算法.实验结果表明,经过AC3_frame_ARR改进后的算法最多可以节省80%的修正检查次数和40%的求解耗时.     

Random Constraint Satisfaction: A More Accurate Picture

In the last few years there has been a great amount of interest in Random Constraint Satisfaction Problems, both from an experimental and a theoretical point of view. Quite intriguingly, experimental results with various models for generating random CSP instances suggest that the probability of such problems having a solution exhibits a threshold–like behavior. In this spirit, some preliminary theoretical work has been done in analyzing these models asymptotically, i.e., as the number of variables grows. In this paper we prove that, contrary to beliefs based on experimental evidence, the models commonly used for generating random CSP instances do not have an asymptotic threshold. In particular, we prove that asymptotically almost all instances they generate are overconstrained, suffering from trivial, local inconsistencies. To complement this result we present an alternative, single–parameter model for generating random CSP instances and prove that, unlike current models, it exhibits non–trivial asymptotic behavior. Moreover, for this new model we derive explicit bounds for the narrow region within which the probability of having a solution changes dramatically.     

基于关联约束非二元弧一致性的约束满足问题求解

弧一致性算法在二元约束满足问题中取得了成功的应用,但并不能被有效泛化至预处理非二元约束满足问题(NCSP).本文提出了处理NCSP的关联约束非二元弧一致性算法.通过随机NCSP生成器产生问题实例,分别采用关联约束非二元弧一致性算法和非二元弧一致性算法进行预处理,并对预处理后的问题实例应用回溯算法进行求解.对比分析采用两种预处理算法和不采用预处理下回溯算法的求解性能,仿真实验结果表明关联约束非二元弧一致性算法可以有效地剔除冗余的约束元组和变量域值,使关联约束非二元弧一致性回溯算法具有更良好的鲁棒性.     

低约束密度分布式约束优化问题的求解算法

多Agent协作过程中的许多挑战都可以建模为分布式约束优化问题.针对低约束密度的分布式约束优化问题,提出了一种基于贪婪和回跳思想的求解算法.在该算法中,各Agent基于贪婪原则进行决策,能够利用低约束密度问题中大量赋值组合代价为0这一特点来加快求解速度.同时,Agent间的回跳机制可以在贪婪原则陷入局部最优时保证算法的完全性.相对于已有主流算法,该算法可以在保持多项式级别的消息长度/空间复杂度的前提下,以较少的消息数目求解低约束密度的分布式约束优化问题.给出了算法关键机制的正确性证明,并通过实验验证了算法的上述性能优势.     

Constraint Reasoning about Repeating Events: Satisfaction and Optimization

Effective manipulation of temporal information about periodic events is required for solving complex problems such as long‐range scheduling or querying temporal information. Furthermore, many problems involving repeating events involve the optimization of temporal aspects of these events (e.g., minimizing make‐span in job‐shop scheduling). In this paper, a constraint‐based formulation of reasoning problems with repeating events is presented, and its complexity is analyzed for a range of problems. Optimization constraints are interpreted formally using the Semiring CSPs (SCSP) representation of optimization in constraint reasoning. This allows for familiar algorithms such as branch‐and‐bound to be applied to solving them.     

Using Constraint Satisfaction for View Update

View update is the problem of translating update requests against a view into update requests against the base data. In this article, we present a novel approach to generating alternative translations of view updates in relational databases. Using conditional tables to represent relational views, we transform a view update problem into constraint satisfaction problems (CSPs). Solutions to the CSPs correspond to possible translations of the view update. Semantic information to resolve ambiguity can be incrementally added as constraints in the CSPs. The connection between view update and constraint satisfaction makes it possible to apply the rich results of the CSP research to analyze and solve the important problem of view management.     

Random Constraint Satisfaction: Flaws and Structure

A recent theoretical result by Achlioptas et al. shows that many models of random binary constraint satisfaction problems become trivially insoluble as problem size increases. This insolubility is partly due to the presence of flawed variables, variables whose values are all flawed (or unsupported). In this paper, we analyse how seriously existing work has been affected. We survey the literature to identify experimental studies that use models and parameters that may have been affected by flaws. We then estimate theoretically and measure experimentally the size at which flawed variables can be expected to occur. To eliminate flawed values and variables in the models currently used, we introduce a flawless generator which puts a limited amount of structure into the conflict matrix. We prove that such flawless problems are not trivially insoluble for constraint tightnesses up to 1/2. We also prove that the standard models B and C do not suffer from flaws when the constraint tightness is less than the reciprocal of domain size. We consider introducing types of structure into the constraint graph which are rare in random graphs and present experimental results with such structured graphs.