Probe Backtrack Search for Minimal Perturbation in Dynamic Scheduling

This paperdescribes an algorithm designed to minimally reconfigure schedulesin response to a changing environment. External factors havecaused an existing schedule to become invalid, perhaps due tothe withdrawal of resources, or because of changes to the setof scheduled activities. The total shift in the start and endtimes of already scheduled activities should be kept to a minimum.This optimization requirement may be captured using a linearoptimization function over linear constraints. However, the disjunctivenature of the resource constraints impairs traditional mathematicalprogramming approaches. The unimodular probing algorithm interleavesconstraint programming and linear programming. The linear programmingsolver handles only a controlled subset of the problem constraints,to guarantee that the values returned are discrete. Using probebacktracking, a complete, repair-based method for search, thesevalues are simply integrated into constraint programming. Unimodularprobing is compared with alternatives on a set of dynamic schedulingbenchmarks, demonstrating its effectiveness.In the final discussion, we conjecture that analogous probebacktracking strategies may obtain performance improvements overconventional backtrack algorithms for a broad range of constraintsatisfaction and optimization problems.     

Adaptive differential dynamic programming for multiobjective optimal control

An efficient numerical solution scheme entitled adaptive differential dynamic programming is developed in this paper for multiobjective optimal control problems with a general separable structure. For a multiobjective control problem with a general separable structure, the “optimal” weighting coefficients for various performance indices are time-varying as the system evolves along any noninferior trajectory. Recognizing this prominent feature in multiobjective control, the proposed adaptive differential dynamic programming methodology combines a search process to identify an optimal time-varying weighting sequence with the solution concept in the conventional differential dynamic programming. Convergence of the proposed adaptive differential dynamic programming methodology is addressed.     

搜索问题的代数描述及其算法

文中给出了搜索问题的一种代数描述方法，在这种描述方法的基础上，给出了一个算法模式。以货郎担问题的动态规划－分支限界法为例，讨论了如何用所给出的算法模式来设计饱含多咱算法设计技术的算法，称这种算法为混合（ｈｙｂｒｉｄ）算法。还讨论了如何用面向对象的方法进行程序设计。这种求解问题的方法不仅为算法的自支生成提供了一种有力的工具，如果用面向对象的方法进行程序设计，还能提高代码的利用率。     

MultiArray: a C++ library for generic programming with arrays

In C++, multi‐dimensional arrays are often used but the language provides limited native support for them. The language, in its Standard Library, supplies sophisticated interfaces for manipulating sequential data, but relies on its bare‐bones C heritage for arrays. The MultiArray library, a part of the Boost library collection, enhances a C++ programmer's tool set with versatile multi‐dimensional array abstractions. It includes a general array class template and native array adaptors that support idiomatic array operations and interoperate with C++ Standard Library containers and algorithms. The arrays share a common interface, expressed as a generic programming concept, in terms of which generic array algorithms can be implemented. We present the library design, introduce a generic interface for array programming, demonstrate how the arrays integrate with the C++ Standard Library, and discuss the essential aspects of their implementation. Copyright © 2004 John Wiley & Sons, Ltd.     

Formulae Meet Programs Over the Net: A Framework for Correct Network Aware Programming

A general framework for network aware programming is presented that consists of a language for programming mobile applications, a logic for specifying properties of the applications and an automatic tool for verifying such properties. The framework is based on X-KLAIM, eXtended KLAIM, an experimental programming language specifically designed to program distributed systems composed of several components interacting through multiple tuple spaces and mobile code. The proposed logic is a modal logic inspired by Hennessy-Milner logic and is interpreted over the same labelled structures used for the operational semantics of X-KLAIM. The automatic verification tool is based on a complete proof system that has been previously developed for the logic.     

HIERARCHICAL ARC CONSISTENCY FOR DISJOINT REAL INTERVALS IN CONSTRAINT LOGIC PROGRAMMING

There have been many proposals for adding sound implementations of numeric processing to Prolog. This paper describes an approach to numeric constraint processing which has been implemented in Echidna, a new constraint logic programming (CLP) language. Echidna uses consistency algorithms which can actively process a wider variety of numeric constraints than most other CLP systems, including constraints containing some common nonlinear functions. A unique feature of Echidna is that it implements domains for real-valued variables with hierarchical data structures and exploits this structure using a hierarchical arc consistency algorithm specialized for numeric constraints. This gives Echidna two advantages over other systems. First, the union of disjoint intervals can be represented directly. Other approaches require trying each disjoint interval in turn during backtrack search. Second, the hierarchical structure facilitates varying the precision of constraint processing. Consequently, it is possible to implement more effective constraint processing control algorithms which avoid unnecessary detailed domain analysis. These advantages distinguish Echidna from other CLP systems for numeric constraint processing.     

Statistical Regimes Across Constrainedness Regions

Much progress has been made in terms of boosting the effectiveness of backtrack style search methods. In addition, during the last decade, a much better understanding of problem hardness, typical case complexity, and backtrack search behavior has been obtained. One example of a recent insight into backtrack search concerns so-called heavy-tailed behavior in randomized versions of backtrack search. Such heavy-tails explain the large variance in runtime often observed in practice. However, heavy-tailed behavior does certainly not occur on all instances. This has led to a need for a more precise characterization of when heavy-tailedness does and when it does not occur in backtrack search. In this paper, we provide such a characterization. We identify different statistical regimes of the tail of the runtime distributions of randomized backtrack search methods and show how they are correlated with the “sophistication” of the search procedure combined with the inherent hardness of the instances. We also show that the runtime distribution regime is highly correlated with the distribution of the depth of inconsistent subtrees discovered during the search. In particular, we show that an exponential distribution of the depth of inconsistent subtrees combined with a search space that grows exponentially with the depth of the inconsistent subtrees implies heavy-tailed behavior. Research supported by the Intelligent Information Systems Institute, Cornell University (AFOSR grant F49620-01-1-0076), MURI (AFOSR grant F49620-01-1-0361) and Ministerio de Educación y Ciencia (TIN-2004 grant 07933-C03-03). We thank the anonymous reviewers for their insightful comments.     

An Overview of Backtrack Search Satisfiability Algorithms

Propositional Satisfiability (SAT) is often used as the underlying model for a significant number of applications in Artificial Intelligence as well as in other fields of Computer Science and Engineering. Algorithmic solutions for SAT include, among others, local search, backtrack search and algebraic manipulation. In recent years, several different organizations of local search and backtrack search algorithms for SAT have been proposed, in many cases allowing larger problem instances to be solved in different application domains. While local search algorithms have been shown to be particularly useful for random instances of SAT, recent backtrack search algorithms have been used for solving large instances of SAT from real-world applications. In this paper we provide an overview of backtrack search SAT algorithms. We describe and illustrate a number of techniques that have been empirically shown to be highly effective in pruning the amount of search on significant and representative classes of problem instances. In particular, we review strategies for non-chronological backtracking, procedures for clause recording and for the identification of necessary variable assignments, and mechanisms for the structural simplification of instances of SAT.     

Efficient local search with conflict minimization: a case study ofthe n-queens problem

Backtracking search is frequently applied to solve a constraint-based search problem, but it often suffers from exponential growth of computing time. We present an alternative to backtracking search: local search with conflict minimization. We have applied this general search framework to study a benchmark constraint-based search problem, the n-queens problem. An efficient local search algorithm for the n-queens problem was implemented. This algorithm, running in linear time, does not backtrack. It is capable of finding a solution for extremely large size n-queens problems. For example, on a workstation it can find a solution for 3000000 queens in less than 55 s     

Top-down design within a functional environment

This paper describes a design tool for use within the INTERLISP1 environment. The design tool, DT, is based upon the design methodology described by the general term, top-down design and is novel in that it attempts to apply this design method to a functional programming environment. Also novel is the method whereby the generation of the design can occur in parallel with the implementation of the design. This allows a design process more in accord with the needs of functional programmers.     

Discrete optimization by relational constraint satisfaction

In pattern matching a basic problem is to determine one or more vectors X that maximize an objective function which is a sum of functions of components of X. When this problem is solved by dynamic programming CPU time and storage requirements grow explosively as the amount of intervariable interaction in the objective function increases. This explosion may be reduced by departing from the traditional dynamic programming method of eliminating successive variables and instead determining a constraint relation between each variable and all others with which it interacts. Discrete relaxation is used to accelerate a backtrack search to find all vectors that satisfy all such constraints. Optimization is achieved by evaluating the objective function for all such vectors. This new method of optimization has been experimentally compared with a classical dynamic programming algorithm running with the same pseudorandomly generated objective functions.     

和声蚁群耦合算法求解整数规划的应用研究

针对于求解一般的整数规划问题,提出了和声蚁群耦合算法。采用和声搜索更新种群策略和个体扰动策略改善了蚁群算法过早收敛的问题,同时采用蚁群算法对寻优路径信息素的正反馈来加快和声搜索收敛于最优路径。实验结果表明,相比于蚁群算法和和声搜索算法,新算法大大提高了一般整数规划问题的搜索效率。     

应用搜索原理解n皇后问题算法分析及优化

n皇后问题是非结构化的问题,人工智能中的搜索策略——回溯法是解决这类问题的有效方法。本文介绍了利用回溯法求解n皇后问题的基本思想以及实现方法,并对算法提出了优化的方法,使得算法的运行效率更高。     

Interactive fuzzy stochastic multi-level 0–1 programming using tabu search and probability maximization

In this paper, we consider interactive fuzzy programming for multi-level 0–1 programming problems involving random variable coefficients both in objective functions and constraints. Following the probability maximization model together with the concept of chance constraints, the formulated stochastic multi-level 0–1 programming problems are transformed into deterministic ones. Taking into account vagueness of judgments of the decision makers, we present interactive fuzzy programming. In the proposed interactive method, after determining the fuzzy goals of the decision makers at all levels, a satisfactory solution is derived efficiently by updating satisfactory levels of the decision makers with considerations of overall satisfactory balance among all levels. For solving the transformed deterministic problems efficiently, we also introduce novel tabu search for general 0–1 programming problems. A numerical example for a three-level 0–1 programming problem is provided to illustrate the proposed method.     

能量限制的连通支配集分布式构造

在无线传感器网络中,通常采用连通支配集来构成一个虚拟骨干网以进行分层路由。在支配集中的节点的耗能相对其它节点要多,虚拟骨干网的生存周期由剩余能量最小的传感器节点决定。提出了一个有能量门限限制的连通支配集分布式构造算法,算法的主要思路是从任一节点开始,采用邻域深度优先搜索,当一个方向搜索结束时再回朔搜索其它方向来形成连通支配集;若某一支配点的邻域中的节点能量小于门限值,则回朔搜索其它方向来形成支配集。     

MATLAB时频分析工具箱在VisualC++程序设计中的应用研究

在分析利用MATLAB函数开发独立可执行VisualC++应用程序的优势和接口技术的基础上,克服以往该方法不能调用MATLAB工具箱函数的不足,探讨了一种能在VisualC++中利用MATLAB时频分析工具箱函数编制独立可执行应用程序的方法。笔者在利用该方法编制旋转机械振动信号时频分析软件收到了良好的效果。     

透视C程序设计语言

从三个不同侧面透视了C高级程序设计语言:"结构化"的程序设计语言;"函数式"的程序设计语言;"面向过程"的程序设计语言。论述了C程序设计语言面向过程的模块化编程思想、用其编程解决实际问题的一般思路和具体程序中的实现形式,同时,总结了上述三个方面的内在联系和它们的统一性。得出了C高级程序设计语言的优点和其强大的生命力,具有较高的概括性和理论意义,对于学习和研究C程序设计语言提供了一个很好的典型案例。     

并发约束程序设计语言COPS及其执行模型

约束程序设计尤其是约束逻辑程序设计与并发约束程序设计在AI程序设计领域占据着越来越重要的位置。传统逻辑程序设计的基“计算即为定理证明”的计算风格虽获得了简洁优美的操作语义特性,但也付出了执行效率低的代价,当应用系统规模增大时,其性能严重下降以致崩溃。针对传统逻辑程序设计的这种可伸缩性问题,设计了一个基于并发约束程序设计概念的说明性语言COPS,旨在从语言设计与执行模型两方面降低说明性程序的不确定性,提高搜索与运行效率。在语言设计方面,通过引入确定性语言成分,避免不确定计算用于确定性目标所浪费的系统开销;在执行模型方面,在目标的并发穿叉执行与数据驱动的并发同步机制的基础上,实现“优先执行确定目标”策略与“最少假定”策略,作为约束传播的延伸,最大幅度地剪枝搜索空间,降低搜索复杂性。COPS提供的知识表示、推理与并发机制使其成为构造agent程序的理想语言。论文给出COPS语言的语法规范与执行模型的操作语义描述。     

基于AC自动机的多模式匹配算法FACA

Aho-Corasick自动机算法在模式匹配失配时,需要多次回溯才转移到有效的后继状态。为此,提出一种快速多模式匹配算法。该算法为每个状态建立失配时的后继指针,在模式匹配失配时,可以通过失配后继指针快速找到有效后继状态,从而避免Aho-Corasick自动机失配时的过多回溯,提高匹配效率。算法在自动机建立时采用动态规划的方法,为每个状态建立匹配长度和匹配量等信息,在模式匹配过程中,基于这些信息统计模式串在主串中的重复次数、最早出现模式串位置等信息。实验结果表明,该算法匹配精确、效率高,且支持在线操作。     

禁忌搜索解决烘干窑调度问题

针对烘干窑生产周期长、成本高的问题,为提高其生产效率,缩短生产周期,对烘干窑的生产调度进行了优化,描述了一个现实中存在的烘干窑调度问题,提出了一种改进的融入了回溯思想的禁忌搜索算法,并给出了算法优化前后的生产计划比较。实际应用结果显示,该算法能有效地缩短生产周期,减少生产成本,提高设备利用率和合同准时率。