This paper is about transforming constraint networks to accommodate additional constraints in specific ways. The focus is on two intertwined issues. First, we investigate how partial solutions to an initial network can be preserved from the potential impact of additional constraints. Second, we study how more permissive constraints, which are intended to enlarge the set of solutions, can be accommodated in a constraint network. These two problems are studied in the general case and the light is shed on their relationship. A case study is then investigated where a more permissive additional constraint is taken into account through a form of network relaxation, while some previous partial solutions are preserved at the same time.

Many formalisms discussed in the literature on qualitative spatial reasoning are designed for expressing static spatial constraints only. However, dynamic situations arise in virtually all applications of these formalisms, which makes it necessary to study variants and extensions involving change. This paper presents a study on the computational complexity of qualitative change. More precisely, we discuss the reasoning task of finding a solution to a temporal sequence of static reasoning problems where this sequence is subject to additional transition constraints. Our focus is primarily on smoothness and continuity constraints: we show how such transitions can be defined as relations and expressed within qualitative constraint formalisms. Our results demonstrate that for point-based constraint formalisms the interesting fragments become NP-completein the presence of continuity constraints, even if the satisfiability problem of its static descriptions is tractable.

Constraint programming techniques are widely used to model and solve interactive decision problems, and especially configuration problems. In this type of application, the configurable product is described by means of a set of constraints bearing on the configuration variables. The user interactively solves the CSP by assigning the variables according to her preferences. The system then has to keep the domains of the other variables consistent with these choices. Since maintaining the global inverse consistency of the domains is not tractable, the domains are instead filtered according to some level of local consistency, e.g. arc-consistency. The present paper aims at offering a more convenient interaction by providing the user with possible alternative values for the already assigned variables, i.e. values that could replace the current ones without leading to a constraint violation. We thus present the new concept of alternative domains in a (possibly) partially assigned CSP. We propose a propagation algorithm that computes all the alternative domains in a single step. Its worst case complexity is comparable to the one of the naive algorithm that would run a full propagation for each variable, but its experimental efficiency is better

Satisfiability Modulo Constraint Handling Rules (SMCHR) is the integration of the Constraint Handling Rules (CHRs) solver programming language into a Satisfiability Modulo Theories (SMT) solver framework.  Constraint solvers are implemented in CHR as a set of high-level rules that specify the simplification (rewriting) and constraint propagation behavior.  The traditional CHR execution algorithm manipulates a global store representing a flat conjunction of constraints.  This paper introduces SMCHR: a tight integration of CHR with a modern Boolean Satisfiability (SAT) solver.  Unlike CHR, SMCHR can handle (quantifier-free) formulae with an arbitrary propositional structure.  SMCHR is essentially a Satisfiability Modulo Theories (SMT) solver where the theory T is implemented in CHR.

The problem of finding the set of pareto optimals for constraints and qualitative preferences together is of great interest to many application areas. It can be viewed as a preference constrained optimization problem where the goal is to find one or more feasible solutions that are not dominated by other feasible outcomes. Our work aims to enhance the current literature of the problem by providing solving methods targeting the problem in a dynamic environments. We target the problem with an eye on adopting and benefiting from the current constraint satisfaction techniques.

We introduce MiningZinc, a general framework for constraint-based pattern mining, one of the most popular tasks in data mining. MiningZinc consists of two key components: a language component and a toolchain component. The language allows for high-level and natu- ral modeling of mining problems, such that MiningZinc models closely resemble definitions found in the data mining literature. It is inspired by the Zinc family of languages and systems and supports user-defined constraints and optimization criteria. The toolchain allows for finding solutions to the models. It ensures the solver independence of the language and supports both standard constraint solvers and specialized data mining systems. Au- tomatic model transformations enable the efficient use of different solvers and systems. The combination of both components allows one to rapidly model constraint-based mining problems and execute these with a wide variety of methods. We demonstrate this experimentally for a number of well-known solvers and data mining tasks.

Despite recent successful real-world deployments of Stackelberg Security Games (SSGs), scale-up remains a fundamental challenge in this field. The latest techniques do not scale-up to domains where multiple defenders must coordinate time-dependent joint activities. To address this challenge, this paper presents two branch-and-price algorithms for solving SSGs, SMARTO and SMARTH, with three novel features: (i) a column-generation approach that uses an ordered network of nodes (determined by solving the traveling salesman problem) to generate individual defender strategies; (ii) exploitation of iterative reward shaping of multiple coordinating defender units to generate coordinated strategies; (iii) generation of tighter upper-bounds for pruning by solving security games that only abide by key scheduling constraints. We provide extensive experimental results and formal analyses.

Distributed Constraint Optimization (DCOP) is a powerful framework for representing and solving distributed combinatorial problems, where the variables of the problem are owned by different agents. Many multi-agent problems include constraints that produce different gains (or costs) for the participating agents. Asymmetric gains of constrained agents cannot be naturally represented by the standard DCOP model. The present paper proposes a general framework for Asymmetric DCOPs (ADCOPs). In ADCOPs different agents may have different valuations for constraints that they are involved in. The new framework bridges the gap between multi-agent problems which tend to have asymmetric structure and the standard symmetric DCOP model. The benefits of the proposed model over previous attempts to generalize the DCOP model are discussed and evaluated. Innovative algorithms that apply to the special properties of the proposed ADCOP model are presented in detail. These include complete algorithms that have a substantial advantage in terms of runtime and network load over existing algorithms (for standard DCOPs) which use alternative representations. Moreover, standard incomplete algorithms (i.e., local search algorithms) are inapplicable to the existing DCOP representations of asymmetric constraints and when they are applied to the new ADCOP framework they often fail to converge to a local optimum and yield poor results. The local search algorithms proposed in the present paper converge to high quality solutions. The experimental evidence that is presented reveals that the proposed local search algorithms for ADCOPs achieve high quality solutions while preserving a high level of privacy.

In this work we are interested in the problem of energy management in Mobile Ad-hoc Network (MANET). The solving and optimization of MANET allow assisting the users to efficiently use their devices in order to minimize the batteries power consumption. In this framework, we propose a modelling of the MANET in form of a Constraint Optimization Problem called COMANET. Then, in the objective to minimize the consumption of batteries power, we present an approach based on an adaptation of the A star algorithm to the MANET problem called MANED. Finally, we expose some experimental results showing utility of this approach.

ExtractingMUCs(MinimalUnsatisfiableCores)fromanunsatisfiable constraint network is a useful process when causes of unsatisfiability must be understood so that the network can be re-engineered and relaxed to become sat- isfiable. Despite bad worst-case computational complexity results, various MUC- finding approaches that appear tractable for many real-life instances have been proposed. Many of them are based on the successive identification of so-called transition constraints. In this respect, we show how local search can be used to possibly extract additional transition constraints at each main iteration step. The approach is shown to outperform a technique based on a form of model rotation imported from the SAT-related technology and that also exhibits additional transi- tion constraints. Our extensive computational experimentations show that this en- hancement also boosts the performance of state-of-the-art DC(WCORE)-like MUC extractors.