The satisfiability problem in regular CNF-formulas

 In this paper we deal with the propositional satisfiability (SAT) problem for a kind of multiple-valued clausal forms known as regular CNF-formulas and extend some known results from classical logic to this kind of formulas. We present a Davis–Putnam-style satisfiability checking procedure for regular CNF-formulas equipped with suitable data structures and prove its completeness. Then, we describe a series of experiments for regular random 3-SAT instances. We observe that, for the regular 3-SAT problem with this procedure, there exists a threshold of the ratio of clauses to variables such that (i) the most computationally difficult instances tend to be found near the threshold, (ii) there is a sharp transition from satisfiable to unsatisfiable instances at the threshold and (iii) the value of the threshold increases as the number of truth values considered increases. Instances in the hard part provide benchmarks for the evaluation of regular satisfiability solvers.     

一种布尔公式的代数逻辑约化新方法

布尔可满足问题是最早被证明的NP完全问题之一,1-in-3-SAT问题是一个NP完全的布尔可满足子类问题。1-in-3-SAT的计算复杂度取决于对应公式的变量以及子句的个数。将1-in-3公式归约为一个变量数或者子句数更少的1-in-3公式,是提高1-in-3-SAT问题求解效率的一个关键。基于一个新的范式形式——XCNF,针对1-in-3-SAT问题提出一种新的代数逻辑约化方法,用于在多项式时间内约减一个1-in-3公式的变量数和子句数。所提算法的主要思想为:首先将1-in-3公式转化为XCNF公式,然后尝试找出XCNF公式中的X-纯文字,并利用X-纯文字法则对1-in-3公式中相应的布尔变量赋值,最后得到一个约减公式,该约减公式与原公式的1-in-3可满足性等价。     

On-line algorithms for polynomially solvable satisfiability problems

Given a propositional Horn formula, we show how to maintain on-line information about its satisfiability during the insertion of new clauses. A data structure is presented which answers each satisfiability question in O(1) time and inserts a new clause of length q in O(q) amortized time. This significantly outperforms previously known solutions of the same problem. This result is extended also to a particular class of non-Horn formulae already considered in the literature, for which the space bound is improved. Other operations are considered, such as testing whether a given hypothesis is consistent with a satisfying interpretation of the given formula and determining a truth assignment which satisfies a given formula. The on-line time and space complexity of these operations is also analyzed.     

The Unique Horn-Satisfiability problem and quadratic Boolean equations

The unique satisfiability problem for general Boolean expressions has attracted interest in recent years in connection with basic complexity issues [12,13]. We investigate here Unique Horn-Satisfiability, i.e. the subclass of Unique-Sat restricted to Horn expressions. We introduce two operators,reduction andshrinking, each transforming a given Horn expression into another Horn expression involving strictly fewer variables and preserving the unique satisfiability property, if present.Uniquely satisfiable Horn expressions are then characterized as those Horn expressions which can be converted into a formula composed of an empty set of clauses on an empty set of free variables through finitely many applications of the shrink-and-reduce operator.Finally, an algorithm for testing whether a given irreducible Horn formula is uniquely satisfiable is described. Data structures for its implementation are discussed, leading toO(mn) complexity for the general case (m=number of clauses,n=number of variables), hence to linear complexity for dense formulae.     

基于消息传递关系网络的布尔可满足性预测

布尔可满足性求解能够验证的问题规模通常受限, 因此, 如何高精度地预测布尔可满足性问题的可满足性是一个重要研究问题, 也是一项具有挑战性的工作.相关研究工作一般使用由文字节点和子句节点组成的图来表示布尔可满足性问题的结构, 但是这种表征方法缺少了变量、子句之间的重要关系信息.在我们的方法中, 通过将原始布尔可满足性问题实例表征为多关系异构图的方式来表达变量和句子之间的关系, 并设计使用消息传递关系网络模型来捕获实例的关系信息, 提取了更多的结构特征.结果表明, 该模型在预测精度、泛化能力和资源需求等方面均优于现有模型, 对所选数据集的平均预测精度为81%.该模型在小规模问题(变量数为100)上训练, 在大规模数据集上预测的平均预测精度达到了80.8%.同时, 该模型对随机生成的非均匀随机问题的预测精度达到99%, 这意味着它学习了预测可满足性的重要特征.此外, 模型预测所花费的时间随着问题规模的增大也只是呈线性增长. 总结而言, 本文基于关系消息传递网络提出了一个预测精度更高, 泛化能力更好的布尔可满足性预测方法.     

A consensus-function artificial neural network for map-coloring

A harmony theory artificial neural network solution to the map coloring problem is presented. Map coloring aims at assigning a unique color to each area of a given map so that no two adjacent areas receive identical colors. The harmony theory implementation is able to determine whether the map coloring problem can be solved with a predefined number of colors as well as which is the smallest number of colors that can solve the map coloring problem. The present implementation directly encodes the given problem into the artificial neural network so that a solution is represented simply by node activation. Additionally, the consensus function of harmony theory produces a quick and definite solution to the colorability problem, obviating the need for manual validation of the result.     

最坏情况下Min-2SAT问题的上界

最坏情况下MaxSAT问题上界的研究已成为一个热门的研究领域.与MaxSAT问题相对的是MinSAT问题,在求解某些组合优化问题时,将其转化为MinSAT问题比转化为MaxSAT问题有着更快的速度,因此对MinSAT问题进行研究.针对Min-2SAT问题提出算法MinSATAlg,该算法首先利用化简算法Simplify对公式进行化简,然后通过分支树的方法对不同情况的子句进行分支.从子句数目的角度分析算法的时间复杂度并证明Min-2SAT问题可在O(1.134 3m)时间内求解,对于每个变量至多出现在3个2-子句中的情况,得到最坏情况下的上界为O(1.122 5n),其中n为变量的数目.     

A Taxonomy of Exact Methods for Partial Max-SAT

Partial Maximum Boolean Satisfiability (Partial Max-SAT or PMSAT) is an optimization variant of Boolean satisfiability (SAT) problem, in which a variable assignment is required to satisfy all hard clauses and a maximum number of soft clauses in a Boolean formula. PMSAT is considered as an interesting encoding domain to many real-life problems for which a solution is acceptable even if some constraints are violated. Amongst the problems that can be formulated as such are planning and scheduling. New insights into the study of PMSAT problem have been gained since the introduction of the Max-SAT evaluations in 2006. Indeed, several PMSAT exact solvers have been developed based mainly on the Davis-Putnam-Logemann-Loveland (DPLL) procedure and Branch and Bound (B&B) algorithms. In this paper, we investigate and analyze a number of exact methods for PMSAT. We propose a taxonomy of the main exact methods within a general framework that integrates their various techniques into a unified perspective. We show its effectiveness by using it to classify PMSAT exact solvers which participated in the 2007～2011 Max-SAT evaluations, emphasizing on the most promising research directions.     

Combining finite learning automata with GSAT for the satisfiability problem

A large number of problems that occur in knowledge-representation, learning, very large scale integration technology (VLSI-design), and other areas of artificial intelligence, are essentially satisfiability problems. The satisfiability problem refers to the task of finding a satisfying assignment that makes a Boolean expression evaluate to True. The growing need for more efficient and scalable algorithms has led to the development of a large number of SAT solvers. This paper reports the first approach that combines finite learning automata with the greedy satisfiability algorithm (GSAT). In brief, we introduce a new algorithm that integrates finite learning automata and traditional GSAT used with random walk. Furthermore, we present a detailed comparative analysis of the new algorithm's performance, using a benchmark set containing randomized and structured problems from various domains.     

An average case analysis of a resolution principle algorithm in mechanical theorem proving

The satisfiability problem is a well known NP-complete problem. In artificial intelligence, solving the satisfiability problem is called mechanical theorem proving. One of those mechanical theorem proving methods is the resolution principle which was invented by J.R. Robinson. In this paper, we shall show how an algorithm based upon the resolution principle can be analyzed. Letn andr ₀ denote the numbers of variables and input clauses respectively. LetP ₀ denote the probability that a variable appears positively, or negatively, in a clause. Our analysis shows that the expected total number of clauses processed by our algorithm isO(n+r ₀) ifP ₀ is a constant,r ₀ is polynomially related withn, andn is large.This research was supported partially by the National Science Council of the Republic of China under the Grant NSC 78-0408-E007-06.     

Exact Algorithms for MAX-SAT

The maximum satisfiability problem (MAX-SAT) is stated as follows: Given a boolean formula in CNF, find a truth assignment that satisfies the maximum possible number of its clauses. MAX-SAT is MAX-SNP-complete and received much attention recently. One of the challenges posed by Alber, Gramm and Niedermeier in a recent survey paper asks: Can MAX-SAT be solved in less than 2ⁿ “steps”? Here, n is the number of distinct variables in the formula and a step may take polynomial time of the input. We answered this challenge positively by showing that a popular algorithm based on branch-and-bound is bounded by O(2ⁿ) in time, where n is the maximum number of occurrences of any variable in the input.When the input formula is in 2-CNF, that is, each clause has at most two literals, MAX-SAT becomes MAX-2-SAT and the decision version of MAX-2-SAT is still NP-complete. The best bound of the known algorithms for MAX-2-SAT is O(m2^m/5), where m is the number of clauses. We propose an efficient decision algorithm for MAX-2-SAT whose time complexity is bound by O(n2ⁿ). This result is substantially better than the previously known results. Experimental results also show that our algorithm outperforms any algorithm we know on MAX-2-SAT.     

多文字可满足SAT问题的相变点上界

可满足(SAT)问题是指：是否存在一组布尔变元赋值,使得随机合取范式(CNF)公式中每个子句至少有1个文字为真。多文字可满足SAT问题是指：是否存在一组布尔变元赋值,使得随机CNF公式中每个子句至少有2个文字为真。此问题仍然是一个NP难问题。定义约束密度α为CNF公式子句数与变元数之比,对该问题的相变点上界α^*进行了研究。如果α>α^*,则多文字可满足SAT问题高概率不可满足。通过一阶矩一个简单的推断,可以证明α^*=-ln 2/ln(1-(k+1)/2^k),当k=3时,α^*=1。利用Kirousis等人的局部最大值技术,提升了多文字可满足3-SAT问题的相变点上界α^*=0.7193。最后,选择了大量数据进行实验验证,结果表明,理论结果与实验结果相吻合。     

基于聚类和划分的SAT分治判定

提出了一种将布尔公式划分为子句组来进行布尔可满足性判定的方法.CNF(conjunctive normal form)公式是可满足的当且仅当划分产生的每个子句组都是可满足的,因此,通过判定子句组的可满足性来判定原公式的可满足性,相当于用分治法将复杂问题分解为多个子问题来求解.这种分治判定方法一方面降低了原公式的可满足性判定复杂度;另一方面,由于子句组的判定可以并行,因而判定速度能够得到进一步的提高.对于不能直接产生布尔子句组划分的情形,提出了一种利用聚类技术将CNF公式聚类成多个簇,然后消去簇间的公共变量来产生子句组划分的方法.     

SAT问题中局部搜索法的改进

局部搜索方法在求解SAT问题的高效率使其成为一研究热点.提出用初始概率的方法对局部搜索算法中变量的初始随机指派进行适当的约束.使在局部搜索的开始阶段,可满足的子句数大大增加,减少了翻转的次数,加快了求解的速度.用该方法对目前的一些重要的SAT问题的局部搜索算法(如WSAT,TSAT,NSAT,SDF等)进行改进,通过对不同规模的随机3-SAT问题的实例和一些不同规模的结构性SAT问题的实例,以及利用相变现象构造的难解SAT实例测试表明,改进后的这些局部搜索算法的求解效率有了很大的提高.该方法对其他局部搜索法的改进具有参考价值.     

Complete Boolean Satisfiability Solving Algorithms Based on Local Search

Boolean satisfiability (SAT) is a well-known problem in computer science, artificial intelligence, and operations research. This paper focuses on the satisfiability problem of Model RB structure that is similar to graph coloring problems and others. We propose a translation method and three effective complete SAT solving algorithms based on the characterization of Model RB structure. We translate clauses into a graph with exclusive sets and relative sets. In order to reduce search depth, we determine search order using vertex weights and clique in the graph. The results show that our algorithms are much more effective than the best SAT solvers in numerous Model RB benchmarks, especially in those large benchmark instances.     

人工神经网络知识增殖性分析

人工神经网络的知识增殖能力是该领域的热点和难点问题,具有重要的理论和实践意义．对人工神经网络的知识增殖性问题进行了较深入的探讨,从网络推广能力的角度分析了具有知识增殖能力的神经网络系统的结构设计问题,指出将多个网络个体结合在一起是实现人工神经网络增殖学习的重要方法,网络的自治能力在此具有重要的意义．利用具有自治能力的神经网络构建的网络群体中,网络个体无需改变而整体具有增殖学习能力,实验结果表明了该方案的可行性．     

A linear programming approach to reasoning about probabilities

We continue our study, initiated in [9], of the following computational problem proposed by Nilsson: Several clauses (Boolean functions of several variables) are given, and for each clause the probability that the clause is true is specified. We are asked whether these probabilities are consistent. They are if there is a probability distribution on the truth assignments such that the probability of each clause is the measure of its satisfying set of assignments. Since this is a generalization of the satisfiability problem of predicate calculus, it is immediately NP-hard. In [9] we showed certain restricted cases of the problem to be NP-complete, and used the Ellipsoid Algorithm to show that a certain special case is in P. In this paper we use the Simplex method, column generation techniques, and variable-depth local search to derive an effective heuristic for the general problem. Experiments show that our heuristic performs successfully on instances with many dozens of variables and clauses. We also prove several interesting complexity results that answer open questions in [9] and motivate our approach.     

基于频次的SAT问题学习子句混合评估算法

为了有效管理学习子句,避免学习子句规模呈几何级增长,减少冗余学习子句对系统内存占用,从而提高布尔可满足性问题SAT求解器的求解效率,需要对学习子句进行评估,然后删减学习子句。传统的评估方式是基于学习子句的长度,保留较短的子句。当前主流的做法一个是变量衰减和VSIDS的子句评估方式,另外一个是基于文字块距离LBD的评估方式,也有将二者结合使用作为子句评估的依据。通过对学习子句参与冲突分析次数与问题求解的关系进行分析,将学习子句使用频率与LBD评估算法混合使用,既反映了学习子句在冲突分析中的作用,也充分利用了文字与决策层之间的信息。以Syrup求解器（GLUCOSE 4.1并行版本）为基准,在评估算法与并行子句共享策略方面做改进测试,通过实验对比发现,混合评估算法比LBD评估算法有优势,求解问题个数明显增多。     

基于Partial MAX-SAT求解法的RBAC授权查询方法

为保证系统的安全性并体现授权的有效性,结合部分最大可满足性问题(Partial MAX-SAT)的研究,提出一种基于Partial MAX-SAT求解法的授权查询方法。使用转换规则将静态授权逻辑和动态互斥角色约束转化为严格子句,采用子句更新算法将满足不同匹配的请求权限转化为松弛子句,并利用子句编码及递归算法寻求真值指派,以满足所有严格子句和尽可能多的松弛子句。实验结果表明,该方法搜索的角色组合能够保证系统的安全性,并满足最小权限分配要求,且最大、精确匹配请求的查询效率优于MAX-SAT求解法。     

An optical solution to the 3-SAT problem using wavelength based selectors

In this paper, an optical wavelength-based method to solve a well-known NP-complete problem 3-SAT is provided. In the 3-SAT problem, a formula F in the form of conjunction of some clauses over Boolean variables is given and the question is to find if F is satisfiable or not. The provided method uses exponential number of different wavelengths in a light ray and considers each group of wavelengths as a possible value-assignment for the variables. It then uses optical devices to drop wavelengths not satisfying F from the light ray. At the end, remaining wavelengths indicate satisfiability of the formula. The method provides two ways to arrange the optical devices to select satisfying wavelengths for a given clause: simple clause selectors and combined clause selectors, both requiring exponential preprocessing time. After preprocessing phase, the provided method requires polynomial time and optical devices to solve each problem instance.