Table Constraints Research Articles

Generalized Hypertree Decomposition for solving non binary CSP with compressed table constraints

Many real-world problems can be modelled as Constraint Satisfaction Problems (CSPs). Although CSP is NP-complete, it has been proved that non binary CSP instances may be efficiently solved if they are representable as Generalized Hypertree Decomposition (GHD) with small width. Algorithms for solving CSPs based on a GHD consider an extensional representation of constraints together with join and semi-join operations giving rise to new large constraint tables (or relations) needed to be temporarily saved. Extensional representation of constraints is quite natural and adapted to the specification of real problems but unfortunately limits significantly the practical performance of these algorithms. The present work tackles this problem using a compact representation of constraint tables. Consequently, to make algorithms compatible with this compact representation, new “compressed join” and “compressed semi-join” operations have to be defined. This last step constitutes our main contribution which, as far as we know, has never been presented. The correctness of this approach is proved and validated on multiple benchmarks. Experimental results show that using compressed relations and compressed operations improves significantly the practical performance of the basic algorithm proposed by Gottlob et al . for solving non binary CSPs with a Generalized Hypertree Decomposition.

Transition Constraints for Parallel Planning

We present a planner named Transition Constraints for Parallel Planning (TCPP). TCPP constructs a new constraint model from domain transition graphs (DTG) of a given planning problem. TCPP encodes the constraint model by using table constraints that allow don't cares or wild cards as cell values. TCPP uses Minion the constraint solver to solve the constraint model and returns the parallel plan. Empirical results exhibit the efficiency of our planning system over state-of-the-art constraint-based planners.

Strong local consistency algorithms for table constraints

Table constraints are important in constraint programming as they are present in many real problems from areas such as configuration and databases. As a result, numerous specialized algorithms that achieve generalized arc consistency (GAC) on table constraints have been proposed. Since these algorithms achieve GAC, they operate on one constraint at a time. In this paper we propose new filtering algorithms for positive table constraints that achieve stronger local consistency properties than GAC by exploiting intersections between constraints. The first algorithm, called maxRPWC+, is a domain filtering algorithm that is based on the local consistency maxRPWC and extends the GAC algorithm of Lecoutre and Szymanek (2006). The second algorithm extends the state-of-the-art STR-based algorithms to stronger relation filtering consistencies, i.e., consistencies that can remove tuples from constraints’ relations. Experimental results from benchmark problems demonstrate that the proposed algorithms are quite competitive with standard GAC algorithms like STR2 in some classes of problems with intersecting table constraints, being orders of magnitude faster in some cases.

STR3: A path-optimal filtering algorithm for table constraints

Constraint propagation is a key to the success of Constraint Programming (CP). The principle is that filtering algorithms associated with constraints are executed in sequence until quiescence is reached. Many such algorithms have been proposed over the years to enforce the property called Generalized Arc Consistency (GAC) on many types of constraints, including table constraints that are defined extensionally. Recent advances in GAC algorithms for extensional constraints rely on directly manipulating tables during search. This is the case with a simple approach called Simple Tabular Reduction (STR), which systematically maintains tables of constraints to their relevant lists of tuples. In particular, STR2, a refined STR variant is among the most efficient GAC algorithms for positive table constraints. In this paper, we revisit this approach by proposing a new GAC algorithm called STR3 that is specifically designed to enforce GAC during backtrack search. By indexing tables and reasoning from deleted values, we show that STR3 can avoid systematically iterating over the full set of current tuples, contrary to STR2. An important property of STR3 is that it can completely avoid unnecessary traversal of tables, making it optimal along any path of the search tree. We also study a variant of STR3, based on an optimal circular way for traversing tables, and discuss the relationship of STR3 with two other optimal GAC algorithms introduced in the literature, namely, GAC4 and AC5TC-Tr. Finally, we demonstrate experimentally how STR3 is competitive with the state-of-the-art. In particular, our extensive experiments show that STR3 is generally faster than STR2 when the average size of tables is not reduced too drastically during search, making STR3 complementary to STR2.

Assessing the influence of climate—water table interactions on jack pine and black spruce productivity in western central Canada

The changes in temperature and precipitation regimes predicted for boreal regions are expected to profoundly affect the productivity of trees situated on waterlogged soils, which underlie large areas of boreal forest in Canada. The consequences for productivity of climatic variation likely depend on site ecology and differ between species. To investigate potential site-dependent responses to climate, we compared the growth of jack pine (Pinus banksiana) trees growing within the same climate conditions but along a transect extending from a fen margin to a sandy ridge. On the fen-margin site, black spruce trees (Picea mariana) were also sampled, allowing for a comparison of the sensitivity of both species. Growth variations at interannual and decadal frequency were analyzed from tree-rings. Our study revealed that the response to climate varied substantially between sites, but, surprisingly, trees growing on the fen margin proved to be very sensitive to fluctuations of precipitation at both interannual and decadal time scales. Black spruce trees responded more sensitively to climate variation than jack pine sampled at the same site. Our study also showed a modest response to temperature even in the driest site. For both species, decadal signals show precipitation as a common, strong productivity driver regardless of water table constraints. These results suggest that the predicted climate warming is therefore less of a threat to the productivity of peatland forested zones than fluctuations in the precipitation regime. Changes in precipitation are expected to have much stronger consequences at both interannual and decadal time scales than projected temperature fluctuations.

Improving the lower bound of simple tabular reduction

Simple Tabular Reduction (STR) is a state-of-the-art filtering technique for enforcing Generalized Arc Consistency (GAC) on positive table constraints. Despite its good performance in practice, the STR2 algorithm suffers from a mandatory lower bound equal to the number of domain values in the current state of the problem, because the presence of each value must be justified every time STR2 is called. We overcome this fixed cost by redesigning STR2 and incorporating watched tuples. Experiments show that the revised algorithm is better at coping with problems that involve a large number of small changes during search and it can be more than twice as fast on certain structured problems.

Database Security Model in the Academic Information System

Database plays an important role on both web-based or desktop based academic information system (AIS) in Indonesian higher education institutions (HEI). Nowadays webbased AIS dominates in Indonesian HEI, almost every HEI uses web-based AIS with relational database management system (RDBMS) as database software. Relational database systems such as Oracle, MySQL, MS SQL Server or PostgreSQL are familiarly used as database management system in the AIS. There are many researches on development of AIS in HEI but none of them is discussing database security and integrity. This research will perform the analysis of database security model that could be used in AIS such as table constraints, table relationships and role-based access control (RBAC).

Optimal and efficient filtering algorithms for table constraints

Filtering algorithms for table constraints can be classified in two categories: constraint-based and value-based. In the constraint-based approaches, the propagation queue only contains information on the constraints that must be reconsidered. For the value-based approaches, the propagation queue also contains information on the removed values. This paper proposes five efficient value-based algorithms for table constraints. Two of them (AC5TCOpt-Tr and AC5TCOpt-Sparse) are proved to have an optimal time complexity of O(r·t+r·d) per table constraint. Substantial experimental results are presented. An empirical analysis is conducted on the effect of the arity of the tables. The experiments show that our propagators are the best when the arity of the table is 3 or 4. Indeed, on instances containing only binary constraints, our algorithms are outperformed by classical AC algorithm AC3rm. AC3rm is dedicated to binary constraints. However, all our algorithms outperform existing state-of-the-art constraint based STR2+ and MDDc and the optimal value-based STR3 algorithms on those instances. On instances with small arity tables (up to arity 4), all our algorithms are generally faster than STR2+, MDDc and than STR3. AC5TCOpt-Sparse is globally the best propagator on those benchmarks. On benchmarks containing large arity tables (arity 5 or more), each of the three existing state-of-the-art algorithms is the winning strategy on one different benchmark.

Extending STR to a Higher-Order Consistency

One of the most widely studied classes of constraints in constraint programming (CP) is that of table constraints. Numerousspecialized filtering algorithms, enforcing the wellknown property called generalized arc consistency (GAC),have been developed for such constraints. Among the most successful GAC algorithms for table constraints, we find variants of simple tabular reduction (STR), like STR2. In this paper,we propose an extension of STR-based algorithms that achieves full pairwise consistency (FPWC), a consistency stronger than GAC and max restricted pairwise consistency (maxRPWC). Our approach involves counting the number of occurrences of specific combinations of values in constraint intersections. Importantly, the worst-case time complexity of one call to the basic filtering procedure at the heart of our new algorithm is quite close to that of STR algorithms. Experiments demonstrate that our method can outperform STR2 in many classes of problems, being significantly faster in some cases. Also, it is clearly superior to maxRPWC+, an algorithm that has been recently proposed.

Making Simple Tabular ReductionWorks on Negative Table Constraints

Simple Tabular Reduction algorithms (STR) work well to establish Generalized Arc Consistency (GAC) on positive table constraints. However, the existing STR algorithms are useless for negative table constraints. In this work, we propose a novel STR algorithm and its improvement, which work on negative table constraints. Our preliminary experiments are performed on some random instances and a certain benchmark instances. The results show that the new algorithms outperform GAC-valid and the MDD-based GAC algorithm.

MDD propagators with explanation

Multi-valued decision diagrams (MDDs) are a convenient approach to representing many kinds of constraints including table constraints, regular constraints, complex set and multiset constraints, as well as ad-hoc problem specific constraints. This paper introduces an incremental propagation algorithm for MDDs, and explores several methods for incorporating explanations with MDD-based propagators. We demonstrate that these techniques can provide significantly improved performance when solving a variety of problems.

STR2: optimized simple tabular reduction for table constraints

Table constraints play an important role within constraint programming. Recently, many schemes or algorithms have been proposed to propagate table constraints and/or to compress their representation. In this paper, we describe an optimization of simple tabular reduction (STR), a technique proposed by J. Ullmann to dynamically maintain the tables of supports when generalized arc consistency (GAC) is enforced/maintained. STR2, the new refined GAC algorithm we propose, allows us to limit the number of operations related to validity checking and search of supports. Interestingly enough, this optimization makes simple tabular reduction potentially r times faster where r is the arity of the constraint(s). The results of an extensive experimentation that we have conducted with respect to random and structured instances indicate that STR2 is usually around twice as fast as the original STR, two or three times faster than the approach based on the hidden variable encoding, and can be up to one order of magnitude faster than previously state-of-the-art (generic) GAC algorithms on some series of instances. When comparing STR2 with the more recently developed algorithm based on multi-valued decision diagrams (MDDs), we show that both approaches are rather complementary.

An MDD-based generalized arc consistency algorithm for positive and negative table constraints and some global constraints

A table constraint is explicitly represented as its set of solutions or non-solutions. This ad hoc (or extensional) representation may require space exponential to the arity of the constraint, making enforcing GAC expensive. In this paper, we address the space and time inefficiencies simultaneously by presenting the mddc constraint. mddc is a global constraint that represents its (non-)solutions with a multi-valued decision diagram (MDD). The MDD-based representation has the advantage that it can be exponentially smaller than a table. The associated GAC algorithm (called mddc) has time complexity linear to the size of the MDD, and achieves full incrementality in constant time. In addition, we show how to convert a positive or negative table constraint into an mddc constraint in time linear to the size of the table. Our experiments on structured problems, car sequencing and still-life, show that mddc is also a fast GAC algorithm for some global constraints such as sequence and regular. We also show that mddc is faster than the state-of-the-art generic GAC algorithms in Gent et al. (2007), Lecoutre and Szymanek (2006), Lhomme and Regin (2005) for table constraint.

Erratum to “Reformulating table constraints using functional dependencies—an application to explanation generation”

Erratum to “Reformulating table constraints using functional dependencies—an application to explanation generation”

CGLIB—a constraint‐based graphics library

AbstractCGLIB is a high‐level graphics library for B‐Prolog, a constraint logic programming system. The library provides primitives for creating and manipulating graphical objects and a set of constraints including not‐overlap, grid, table and tree constraints that facilitate the specification of the layouts of objects. The library adopts a construct called action rules which is available in B‐Prolog for creating agents and programming interactions among agents or between agents and the user. The library is a fully working system implemented in B‐Prolog, Java and C. It can be used in many areas such as drawing editors, interactive user interfaces, document authoring, animation, information visualization, intelligent agents and games. The high‐level abstraction of the library and the use of constraints and action rules in the specification of layouts and behaviors can significantly enhance the productivity of the development of graphics. We demonstrate through several examples the effectiveness of the library as a tool for developing graphics‐rich and interactive user interfaces. Copyright © 2003 John Wiley & Sons, Ltd.