Constraint Propagation Research Articles

A framework for step-wise explaining how to solve constraint satisfaction problems

We explore the problem of step-wise explaining how to solve constraint satisfaction problems, with a use case on logic grid puzzles. More specifically, we study the problem of explaining the inference steps that one can take during propagation, in a way that is easy to interpret for a person. Thereby, we aim to give the constraint solver explainable agency, which can help in building trust in the solver by being able to understand and even learn from the explanations. The main challenge is that of finding a sequence of simple explanations, where each explanation should aim to be as cognitively easy as possible for a human to verify and understand. This contrasts with the arbitrary combination of facts and constraints that the solver may use when propagating. We propose the use of a cost function to quantify how simple an individual explanation of an inference step is, and identify the explanation-production problem of finding the best sequence of explanations of a CSP. Our approach is agnostic of the underlying constraint propagation mechanisms, and can provide explanations even for inference steps resulting from combinations of constraints. In case multiple constraints are involved, we also develop a mechanism that allows to break the most difficult steps up and thus gives the user the ability to zoom in on specific parts of the explanation. Our proposed algorithm iteratively constructs the explanation sequence by using an optimistic estimate of the cost function to guide the search for the best explanation at each step. Our experiments on logic grid puzzles show the feasibility of the approach in terms of the quality of the individual explanations and the resulting explanation sequences obtained.

Constraint Satisfaction for Motion Feasibility Checking

Task and motion planning (TAMP) is a key research field for robotic manipulation tasks. The goal of TAMP is to generate motion-feasible task plan automatically. Existing methods for checking motion feasibility of task plan skeletons have some limitations of semantic-free pose candidate sampling, weak search heuristics, and early value commitment. In order to overcome these limitations, we propose a novel constraint satisfaction framework for checking motion feasibility of task plan skeletons. Our framework provides (1) a semantic pose candidate sampling method, (2) novel variable and constraint ordering heuristics based on intra- and inter-action dependencies in a task plan skeleton, and (3) an efficient search strategy using constraint propagation. Based upon these techniques, our framework can improve the efficiency of motion feasibility checking for TAMP. From experiments using the humanoid robot PR2, we show that the motion feasibility checking in our framework is 1.4x to 6.0x faster than previous ones.

Adaptive constraint propagation in constraint satisfaction: review and evaluation

Adaptive constraint propagation in constraint satisfaction: review and evaluation

Bus-Based VANET using ACO Multipath Routing Algorithm

Vehicular ad-hoc Network (VANET) for buses provides a good solution with inconsistent and high dynamic structure of a typical network. In this work, Street-centric routing algorithm is used for internet of energy for the bus-based VANET to optimize relay bus and route selection issues. As the initial step, we have used a multipath routing system that uses probability of path consistency and street consistency. This methodology is observed to positively improve end-to-end delay and also the packet delivery ratio. The next step is to optimize the characteristics of packet forwarding by means of an Ant Colony Optimization (ACO) methodology as the mechanism for relay-bus selection. The main goal of this paper is to transmit packets to the next forwarding relay with the help of relay-bus. On experimental analysis, the results indicate that there is a significant improvement in relay-bus choosing by reducing the end-to-end delay, cost of computation and unwanted beacon messages. Based on the analysis, we have arrived at the conclusion that the use of clustering and multipath will positively improve the performance of the proposed work.

An order independent algorithm for inferring gene regulatory network using quantile value for conditional independence tests

In recent years, due to the difficulty and inefficiency of experimental methods, numerous computational methods have been introduced for inferring the structure of Gene Regulatory Networks (GRNs). The Path Consistency (PC) algorithm is one of the popular methods to infer the structure of GRNs. However, this group of methods still has limitations and there is a potential for improvements in this field. For example, the PC-based algorithms are still sensitive to the ordering of nodes i.e. different node orders results in different network structures. The second is that the networks inferred by these methods are highly dependent on the threshold used for independence testing. Also, it is still a challenge to select the set of conditional genes in an optimal way, which affects the performance and computation complexity of the PC-based algorithm. We introduce a novel algorithm, namely Order Independent PC-based algorithm using Quantile value (OIPCQ), which improves the accuracy of the learning process of GRNs and solves the order dependency issue. The quantile-based thresholds are considered for different orders of CMI tests. For conditional gene selection, we consider the paths between genes with length equal or greater than 2 while other well-known PC-based methods only consider the paths of length 2. We applied OIPCQ on the various networks of the DREAM3 and DREAM4 in silico challenges. As a real-world case study, we used OIPCQ to reconstruct SOS DNA network obtained from Escherichia coli and GRN for acute myeloid leukemia based on the RNA sequencing data from The Cancer Genome Atlas. The results show that OIPCQ produces the same network structure for all the permutations of the genes and improves the resulted GRN through accurately quantifying the causal regulation strength in comparison with other well-known PC-based methods. According to the GRN constructed by OIPCQ, for acute myeloid leukemia, two regulators BCLAF1 and NRSF reported previously are significantly important. However, the highest degree nodes in this GRN are ZBTB7A and PU1 which play a significant role in cancer, especially in leukemia. OIPCQ is freely accessible at https://github.com/haammim/OIPCQ-and-OIPCQ2.

A Path-Oriented Automatic Random Testing Based on Double Constraint Propagation

A Path-Oriented Automatic Random Testing Based on Double Constraint Propagation

Reasoning and querying bounds on differences with layered preferences

Artificial intelligence largely relies on bounds on differences (BoDs) to model binary constraints regarding different dimensions, such as time, space, costs, and calories. Recently, some approaches have extended the BoDs framework in a fuzzy, “noncrisp” direction, considering probabilities or preferences. While previous approaches have mainly aimed at providing an optimal solution to the set of constraints, we propose an innovative class of approaches in which constraint propagation algorithms aim at identifying the “space of solutions” (i.e., the minimal network) with their preferences, and query answering mechanisms are provided to explore the space of solutions as required, for example, in decision support tasks. Aiming at generality, we propose a class of approaches parametrized over user-defined scales of qualitative preferences (e.g., Low, Medium, High, and Very High), utilizing the resume and extension operations to combine preferences, and considering different formalisms to associate preferences with BoDs. We consider both “general” preferences and a form of layered preferences that we call “pyramid” preferences. The properties of the class of approaches are also analyzed. In particular, we show that, when the resume and extension operations are defined such that they constitute a closed semiring, a more efficient constraint propagation algorithm can be used. Finally, we provide a preliminary implementation of the constraint propagation algorithms.

A Hybrid APM-CPGSO Approach for Constraint Satisfaction Problem Solving: Application to Remote Sensing

Constraint satisfaction problem (CSP) has been actively used for modeling and solving a wide range of complex real-world problems. However, it has been proven that developing efficient methods for solving CSP, especially for large problems, is very difficult and challenging. Existing complete methods for problem-solving are in most cases unsuitable. Therefore, proposing hybrid CSP-based methods for problem-solving has been of increasing interest in the last decades. This paper aims at proposing a novel approach that combines incomplete and complete CSP methods for problem-solving. The proposed approach takes advantage of the group search algorithm (GSO) and the constraint propagation (CP) methods to solve problems related to the remote sensing field. To the best of our knowledge, this paper represents the first study that proposes a hybridization between an improved version of GSO and CP in the resolution of complex constraint-based problems. Experiments have been conducted for the resolution of object recognition problems in satellite images. Results show good performances in terms of convergence and running time of the proposed CSP-based method compared to existing state-of-the-art methods.

Интеграция методов поиска в ширину и логического вывода для удовлетворения табличных ограничений

Within the Constraint Programming technology, so-called table constraints such as typical tables, compressed tables, smart tables, segmented tables, etc, are widely used. They can be used to represent any other types of constraints, and algorithms of the table constraint propagation (logical inference on constraints) allow eliminating a lot of "redundant" values from the domains of variables, while having low computational complexity. In the previous studies, the author proposed to divide smart tables into structures of C- and D-types. The generally accepted methodology for solving constraint satisfaction problems is the combined application of constraint propagation methods and backtracking depth-first search methods. In the study, it is proposed to integrate breadth-first search methods and author`s method of table constraint propagation. D-type smart tables are proposed to be represented as a join of several orthogonalized C-type smart tables. The search step is to select a pair of C-type smart tables to be joined and then propagate the restrictions. To determine the order of joining orthogonalized smart tables at each step of the search, a specialized heuristic is used, which reduces the search space, taking into account further calculations. When the restrictions are extended, the acceleration of the computation process is achieved by applying the developed reduction rules for the case of C-type smart tables. The developed hybrid method allows one to find all solutions to the problems of satisfying constraints modeled using one or several D-type smart tables, without decomposing tabular constraints into elementary tuples.

Data representation using robust nonnegative matrix factorization for edge computing.

As a popular data representation technique, Nonnegative matrix factorization (NMF) has been widely applied in edge computing, information retrieval and pattern recognition. Although it can learn parts-based data representations, existing NMF-based algorithms fail to integrate local and global structures of data to steer matrix factorization. Meanwhile, semi-supervised ones ignore the important role of instances from different classes in learning the representation. To solve such an issue, we propose a novel semi-supervised NMF approach via joint graph regularization and constraint propagation for edge computing, called robust constrained nonnegative matrix factorization (RCNMF), which learns robust discriminative representations by leveraging the power of both L2, 1-norm NMF and constraint propagation. Specifically, RCNMF explicitly exploits global and local structures of data to make latent representations of instances involved by the same class closer and those of instances involved by different classes farther. Furthermore, RCNMF introduces the L2, 1-norm cost function for addressing the problems of noise and outliers. Moreover, L2, 1-norm constraints on the factorial matrix are used to ensure the new representation sparse in rows. Finally, we exploit an optimization algorithm to solve the proposed framework. The convergence of such an optimization algorithm has been proven theoretically and empirically. Empirical experiments show that the proposed RCNMF is superior to other state-of-the-art algorithms.

Solving CSPs Using Weak Local Consistency

The characterization of all the constraint satisfaction problems solvable by local consistency checking (also known as CSPs of bounded width) was proposed by Feder and Vardi [SIAM J. Comput., 28 (1998), pp. 57--104]. It was confirmed by two independent proofs by Bulatov [Bounded Relational Width, manuscript, 2009] and Barto and Kozik [L. Barto and M. Kozik, 50th Annual IEEE Symposium on Foundations of Computer Science, 2009, pp. 595--603], [L. Barto and M. Kozik, J. ACM, 61 (2014), 3]. Later Barto [J. Logic Comput., 26 (2014), pp. 923--943] proved a collapse of the hierarchy of local consistency notions by showing that (2,3) minimality solves all the CSPs of bounded width. In this paper we present a new consistency notion, jpq consistency, which also solves all the CSPs of bounded width. Our notion is strictly weaker than (2,3) consistency, (2,3) minimality, path consistency, and singleton arc consistency (SAC). This last fact allows us to answer the question of Chen, Dalmau, and Grußien [J. Logic Comput., 23 (2013), pp. 87--108] by confirming that SAC solves all the CSPs of bounded width. Moreover, as known algorithms work faster for SAC, the result implies that CSPs of bounded width can be, in practice, solved more efficiently. The definition of jpq consistency is closely related to a consistency condition obtained from the rounding of an SDP relaxation of a CSP instance. In fact, the main result of this paper is used by Dalmau et al. [Proceedings of the 28th Annual ACM-SIAM Symposium on Discrete Algorithms, SIAM, Philadelphia, ACM, New York, 2017, pp. 340--357] to show that CSPs with near unanimity polymorphisms admit robust approximation algorithms with polynomial loss. Finally, an algebraic characterization of some term conditions satisfied in algebras associated with templates of bounded width, first proved by Brady, is a direct consequence of our result.

Under the same (Chole)sky: DNK models, timing restrictions and recursive identification of monetary policy shocks

Recent structural VAR studies of the monetary transmission mechanism have voiced concerns about the use of recursive identification schemes based on short-run exclusion restrictions. We trace out the effects on impulse propagation of informational constraints embodying classical Cholesky-timing restrictions in otherwise standard Dynamic New Keynesian (DNK) models. By reinforcing internal propagation mechanisms and enlarging a model's equilibrium state space, timing restrictions may produce a non-trivial moving average component of the equilibrium representation, making finite order VARs a poor approximation of true adjustment paths to monetary impulses, albeit correctly identified. They can even serve as an independent source of model-based nonfundamentalness, thereby hampering shock identification via VAR methods. This notwithstanding, restricted DNK models are shown to feature (i) invertible equilibrium representations for the observables and (ii) fast-converging VAR coefficient matrices under empirically tenable parameterizations. This alleviates concerns about identification and lag truncation bias: low-order Cholesky-VARs do well at uncovering the transmission of monetary impulses in a truly Cholesky world.

Embedded Propagation Graph Model for Reflection and Scattering and Its Millimeter-Wave Measurement-Based Evaluation

Propagation graph (PG) is a stochastic channel simulation method for scattering propagation. In this article, an embedded-PG (EPG) approach, an extension of conventional PG, is proposed to simulate reflection and scattering multi-path behaviors in wireless channels. In this method, multiple propagation paths are categorized into scattering-path, reflecting-path, and scattering-reflecting mixed paths among reflectors and scatterers. The matrix recursive formula of conventional PG modeling is used to calculate scattering-path, a recursive mathematical transformation is applied to adapt reflecting-path into the recursive formula, and an embedded graph method is used to decompose mixed-path into scattering effects and reflection effects. The proposed simulation approach is validated by comparison with conventional PG and measurement in 39 GHz millimeter-wave (mm-wave) time-variant corridor scenario. Power delay profiles (PDPs) and spatial consistency of multiple paths observed in concatenated-PDPs (CPDPs) obtained by EPG are more consistent with measurement than conventional PG, differences of mean delay and delay spread between simulations and measurement in typical snapshots are within 3 ns and 1.5 ns, respectively.

Interval state estimation based on constraint propagation for a lithium-ion cell using an equivalent circuit model

Abstract Battery management systems (BMSs) are responsible for controlling and monitoring battery operations. In order to be effective, BMSs rely on mathematical models. The parameters of these latter are usually obtained through an identification procedure which provides limited accuracy. In addition, only cell current and voltage are usually measurable. For this reason, the model states need to be estimated. In the following, we propose an interval state estimation of a lithium-ion cell when using an equivalent circuit model, taking into account bounded parametric uncertainty and measurement noise. Simulation results show the suitability of the proposed interval estimator to provide a tight enclosure of the states, which is essential for fault detection and model-based control of a lithium-ion cell.

A Conditional Time-Intervals formulation of the real-time Railway Traffic Management Problem

This paper tackles the real-time Railway Traffic Management Problem (rtRTMP). It is the problem of finding an optimal choice for the train schedules and routes to reduce the delays of trains due to conflicts. We present a new Constraint Based Scheduling (CBS) formulation of the rtRTMP. This new formulation is based on the concept of conditional time-interval variables provided in the Ilog CP-optimizer library. A time-interval variable is the time interval in which an activity is executed, but it can also be a specific value "⊥" meaning the activity is non-executed. The new formulation exploits this new kind of variables and specific constraint propagation algorithms which contribute to the efficiency of the solution methods. The formulation has been validated with experiments on a large set of instances. The experimental results demonstrate the effectiveness of this new CBS model and show its good performance compared with the state-of-the art RECIFE-MILP algorithm.

Measuring shallow-water bathymetric signal strength in lidar point attribute data using machine learning

ABSTRACT The goal of this work was to evaluate if routinely collected but seldom used airborne lidar metadata – ‘point attribute data’ (PAD) – analyzed using machine learning/artificial intelligence can improve extraction of shallow-water (less than 20 m) bathymetry from lidar point clouds. Extreme gradient boosting (XGB) models relating PAD to an existing bathymetry/not bathymetry classification were fitted and evaluated for four areas near the Florida Keys. The PAD examined include ‘pulse specific’ information such as the return intensity and PAD describing flight path consistency. The R2 values for the XGB models were between 0.34 and 0.74. Global classification accuracies were above 80% although this reflected a sometimes extreme Bathy/NotBathy imbalance that inflated global accuracy. This imbalance was mitigated by employing a probability decision threshold (PDT) that equalizes the true positive (Bathy) and true negative (NotBathy) rates. It was concluded that 1) the strength of the bathymetric signal in the PAD should be sufficient to increase accuracy of density-based lidar point cloud bathymetry extraction methods and 2) ML can successfully model the relationship between the PAD and the Bathy/NotBathy classification. A method is also presented to examine the spatial and feature-space distribution of errors that will facilitate quality assurance and continuous improvement.

Semi-supervised kernel matrix learning using adaptive constraint-based seed propagation

In this paper, we propose semi-supervised kernel matrix learning (SS-KML) using adaptive constraint-based seed propagation (ACSP). Conventional SS-KML methods such as pairwise constraint propagation (PCP) and kernel propagation (KP) have achieved outstanding performance in data classification. However, they are likely to distort the global data structure because of using hard constraints in their semi-definite problems (SDPs) for constraint propagation. Moreover, given a large number of pairwise constraints and a large amount of samples, they tend to be incredibly complex, thus being hard to be applied to real-life complex problems such as internet-scale image categorization. To address this problem, we utilize adaptive constraints to effectively maintain the inherent coherence of samples and successfully propagate constraint information into all samples. Moreover, we adopt seed propagation to remarkably reduce the computational complexity of SS-KML. Experimental results demonstrate that ACSP achieves a significant improvement in performance over PCP and KP in terms of both effectiveness and efficiency.

Multiview clustering via exclusive non-negative subspace learning and constraint propagation

Multiview clustering partitions a set of data into groups by exploring complementary information of multiple views. The mainstream tries to project the multiview data into a commonly shared subspace and further discover the true data structure. Ideally, clusters in the subspace should share less semantics with each other so that distinct groups can be obtained while this exclusivity is not guaranteed in previous works. To this end, this paper proposes a non-negative matrix factorization based subspace learning method for exclusive multiview clustering, where the double-orthogonal constraints are imposed for the cluster exclusivity. Moreover, to boost the clustering performance, the proposed method also exploits the available labeled data and is extended into a semi-supervised manner. Particularly, by incorporating the propagated semi-supervised manifold regularizations, the limited supervised information is enriched and encoded in our method to guide the learning process. The formulated optimization problem can be solved by the derived iterative updating rules. Experimental results on seven public datasets demonstrate its promising performance against other state-of-the-art approaches.

Incorporating bounds from decision diagrams into integer programming

Decision diagrams have been successfully used to help solve several classes of discrete optimization problems. We explore an approach to incorporate them into integer programming solvers, motivated by the wide adoption of integer programming technology in practice. The main challenge is to map generic integer programming models to a recursive structure that is suitable for decision diagram compilation. We propose a framework that opportunistically constructs decision diagrams for suitable substructures, if present. In particular, we explore the use of a prevalent substructure in integer programming solvers known as the conflict graph, which we show to be amenable to decision diagrams. We use Lagrangian relaxation and constraint propagation to consider constraints that are not represented directly by the substructure. We use the decision diagrams to generate dual and primal bounds to improve the pruning process of the branch-and-bound tree of the solver. Computational results on the independent set problem with side constraints indicate that our approach can provide substantial speedups when conflict graphs are present.

An exemplar-based clustering using efficient variational message passing

Clustering is a crucial step in scientific data analysis and engineering systems. Thus, an efficient cluster analysis method often remains a key challenge. In this paper, we introduce a general purpose exemplar-based clustering method called (MEGA), which performs a novel message-passing strategy based on variational expectation–maximization and generalized arc-consistency techniques. Unlike message passing clustering methods, MEGA formulates the message-passing schema as E- and M-steps of variational expectation–maximization based on a reparameterized factor graph. It also exploits an adaptive variant of generalized arc consistency technique to perform a variational mean-field approximation in E-step to minimize a Kullback–Leibler divergence on the model evidence. Dissimilar to density-based clustering methods, MEGA has no sensitivity to initial parameters. In contrast to partition-based clustering methods, MEGA does not require pre-specifying the number of clusters. We focus on the binary-variable factor graph to model the clustering problem but MEGA is applicable to other graphical models in general. Our experiments on real-world problems demonstrate the efficiency of MEGA over existing prominent clustering algorithms such as Affinity propagation, Agglomerative, DBSCAN, K-means, and EM.