Efficient path consistency algorithm for large qualitative constraint networks

We propose a new algorithm called DPC+ to enforce partial path consistency (PPC) on qualitative constraint networks. PPC restricts path consistency (PC) to a triangulation of the underlying constraint graph of a network. As PPC retains the sparseness of a constraint graph, it can make reasoning tasks such as consistency checking and minimal labelling of large qualitative constraint networks much easier to tackle than PC. For qualitative constraint networks defined over any distributive subalgebra of well-known spatio-temporal calculi, such as the Region Connection Calculus and the Interval Algebra, we show that DPC+ can achieve PPC very fast. Indeed, the algorithm enforces PPC on a qualitative constraint network by processing each triangle in a triangulation of its underlying constraint graph at most three times. Our experiments demonstrate significant improvements of DPC+ over the state-of-the-art PPC enforcing algorithm

On tree-preserving constraints

© Springer International Publishing Switzerland 2015. Tree convex constraints are extensions of the well-known row convex constraints. Just like the latter, every path-consistent tree convex constraint network is globally consistent. This paper studies and compares three subclasses of tree convex constraints which are called chain-, path- and tree-preserving constraints respectively. While the tractability of the subclass of chain-preserving constraints has been established before, this paper shows that every chain- or path-preserving constraint network is in essence the disjoint union of several independent connected row convex constraint networks, and hence (re-)establish the tractability of these two subclasses of tree convex constraints. We further prove that, when enforcing arc- and path-consistency on a tree-preserving constraint network, in each step, the network remains tree-preserving. This ensures the global consistency of the tree-preserving network if no inconsistency is detected. Moreover, it also guarantees the applicability of the partial path-consistency algorithm to tree-preserving constraint networks, which is usually more efficient than the path-consistency algorithm for large sparse networks. As an application, we show that the class of treepreserving constraints is useful in solving the scene labelling problem

Arc and path consistency revisited

Journal ArticleMackworth and Freuder have analyzed the time complexity of several constraint satisfaction algorithms [4]. We present here new algorithms for arc and path consistency and show that the arc consistency algorithm is optimal in time complexity and of the same order space complexity as the earlier algorithms. A refined solution for the path consistency problem is proposed. However, the space complexity of the path consistency algorithm makes it practicable only for small problems. These algorithms are the result of the synthesis techniques used in ALICE (a general constraint satisfaction system) and local consistency methods

Efficient Path Consistency Algorithms for Constraint Satisfaction Problems

A large number of problems can be formulated as special cases of the Constraint Satisfaction Problem (CSP). In such a problem, the task specification can be formulated to consist of a set of variables, a domain for each variable and a set of constraints on these variables. A typical task is then to find an instantiation of these variables (to values in their respective domains) such that all the constraints are simultaneously satisfied. Most of the methods used to solve such problems are based on some backtracking scheme, which can be very inefficient with exponential run-time complexity for most nontrivial problems. Path consistency algorithms constitute an important class of algorithms used to simplify the search space, either before or during search, by eliminating inconsistent values from the domains of the corresponding variables. However, the use of these algorithms in real life applications has been limited, mainly, due to their high space complexity. Han and Lee [5] presented a path consistency algorithm, PC-4, with 0(n3a3) space complexity, which makes it practicable only for small problems. I present a new path consistency algorithm, PC-5, which has an 0(n3a2) space complexity while retaining the worst-case time complexity of PC-4. Moreover, the new algorithm exhibits a much better average-case time complexity. The new algorithm is based on the idea (due to Bessiere [1]) that, at any time, only a minimal amount of support has to be found and recorded for a labeling to establish its viability; one has to look for a new support only if the current support is eliminated. I also show that PC-5 can be improved further to yield an algorithm, PC5++, with even better average-case performance and the same space complexity. I present experimental results evaluating the performance of these algorithms on various classes of problems. The results show that both PC-5 and PC5++ significantly outperform PC-4, both in terms of space and time, with PC5++ being the better of the two algorithms presented

The Role of Commutativity in Constraint Propagation Algorithms

Constraint propagation algorithms form an important part of most of the constraint programming systems. We provide here a simple, yet very general framework that allows us to explain several constraint propagation algorithms in a systematic way. In this framework we proceed in two steps. First, we introduce a generic iteration algorithm on partial orderings and prove its correctness in an abstract setting. Then we instantiate this algorithm with specific partial orderings and functions to obtain specific constraint propagation algorithms. In particular, using the notions commutativity and semi-commutativity, we show that the {\tt AC-3}, {\tt PC-2}, {\tt DAC} and {\tt DPC} algorithms for achieving (directional) arc consistency and (directional) path consistency are instances of a single generic algorithm. The work reported here extends and simplifies that of Apt \citeyear{Apt99b}.Comment: 35 pages. To appear in ACM TOPLA

MUSE CSP: An Extension to the Constraint Satisfaction Problem

This paper describes an extension to the constraint satisfaction problem (CSP) called MUSE CSP (MUltiply SEgmented Constraint Satisfaction Problem). This extension is especially useful for those problems which segment into multiple sets of partially shared variables. Such problems arise naturally in signal processing applications including computer vision, speech processing, and handwriting recognition. For these applications, it is often difficult to segment the data in only one way given the low-level information utilized by the segmentation algorithms. MUSE CSP can be used to compactly represent several similar instances of the constraint satisfaction problem. If multiple instances of a CSP have some common variables which have the same domains and constraints, then they can be combined into a single instance of a MUSE CSP, reducing the work required to apply the constraints. We introduce the concepts of MUSE node consistency, MUSE arc consistency, and MUSE path consistency. We then demonstrate how MUSE CSP can be used to compactly represent lexically ambiguous sentences and the multiple sentence hypotheses that are often generated by speech recognition algorithms so that grammar constraints can be used to provide parses for all syntactically correct sentences. Algorithms for MUSE arc and path consistency are provided. Finally, we discuss how to create a MUSE CSP from a set of CSPs which are labeled to indicate when the same variable is shared by more than a single CSP.Comment: See http://www.jair.org/ for any accompanying file

Inferring gene regulatory networks from gene expression data by path consistency algorithm based on conditional mutual information

Motivation: Reconstruction of gene regulatory networks (GRNs), which explicitly represent the causality of developmental or regulatory process, is of utmost interest and has become a challenging computational problem for understanding the complex regulatory mechanisms in cellular systems. However, all existing methods of inferring GRNs from gene expression profiles have their strengths and weaknesses. In particular, many properties of GRNs, such as topology sparseness and non-linear dependence, are generally in regulation mechanism but seldom are taken into account simultaneously in one computational method.Results: In this work, we present a novel method for inferring GRNs from gene expression data considering the non-linear dependence and topological structure of GRNs by employing path consistency algorithm (PCA) based on conditional mutual information (CMI). In this algorithm, the conditional dependence between a pair of genes is represented by the CMI between them. With the general hypothesis of Gaussian distribution underlying gene expression data, CMI between a pair of genes is computed by a concise formula involving the covariance matrices of the related gene expression profiles. The method is validated on the benchmark GRNs from the DREAM challenge and the widely used SOS DNA repair network in Escherichia coli. The cross-validation results confirmed the effectiveness of our method (PCA-CMI), which outperforms significantly other previous methods. Besides its high accuracy, our method is able to distinguish direct (or causal) interactions from indirect associations