Constraint Programming Language Research Articles

Constraint Programming Approach to Coverage-Path Planning for Autonomous Multi-UAV Infrastructure Inspection

This article presents a constraint modeling approach to global coverage-path planning for linear-infrastructure inspection using multiple autonomous UAVs. The problem is mathematically formulated as a variant of the Min–Max K-Chinese Postman Problem (MM K-CPP) with multi-weight edges. A high-level constraint programming language is used to model the problem, which enables model execution with different third-party solvers. The optimal solutions are obtained in a reasonable time for most of the tested instances and different numbers of vehicles involved in the inspection. For some graphs with multi-weight edges, a time limit is applied, as the problem is NP-hard and the computation time increases exponentially. Despite that, the final total inspection cost proved to be lower when compared with the solution obtained for the unrestricted MM K-CPP with single-weight edges. This model can be applied to plan coverage paths for linear-infrastructure inspection, resulting in a minimal total inspection time for relatively simple graphs that resemble real transmission networks. For more extensive graphs, it is possible to obtain valid solutions in a reasonable time, but optimality cannot be guaranteed. For future improvements, further optimization could be considered, or different models could be developed, possibly involving artificial neural networks.

Optimal capacity utilisation of transmission lines network in power flow operations: a survey

Purpose This paper aims to survey the need for full capacity utilisation of transmission lines in power systems network operations. It proposes a review of the N-1 security criterion that does not ensure reliable dispatch of optimum power flow during outage contingency. The survey aims to enlarge the network capacity utilisation to rely on the entire transmission lines network operation. Design/methodology/approach The paper suggests transmission line switching (TLS) approach as a viable corrective mechanism for power dispatch. The TLS process is incorporated into a constraint programming language extension optimisation solver that selects the switchable line candidates as integer variables in the mixed integer programming problem. Findings The paper provides a practical awareness of reserve capacity in the lines that provide network security in outage contingency. At optimum power flow dispatch, the TLS is extended to optimal transmission line switching (OTLS) that indicates optimal capacity utilisation (OCU) of the available reserve capacity (ARC) in the network lines. Practical implications Computational efficiency influenced the extension of the OTLS to optimal transmission switching of power flow (OTSPF). The application of OTSPF helps reduce the use of flexible AC transmission systems (FACTS) and construction of new transmission lines.. Originality/value The paper surveys TLS efforts in network capacity utilisation. The suggested ARC fulfils the need for an index with which the dispatchable lines may be identified for the optimal capacity utilisation of transmission lines network.

How People Visually Represent Discrete Constraint Problems.

Problems such as timetabling or personnel allocation can be modeled and solved using discrete constraint programming languages. However, while existing constraint solving software solves such problems quickly in many cases, these systems involve specialized languages that require significant time and effort to learn and apply. These languages are typically text-based and often difficult to interpret and understand quickly, especially for people without engineering or mathematics backgrounds. Visualization could provide an alternative way to model and understand such problems. Although many visual programming languages exist for procedural languages, visual encoding of problem specifications has not received much attention. Future problem visualization languages could represent problem elements and their constraints unambiguously, but without unnecessary cognitive burdens for those needing to translate their problem's mental representation into diagrams. As a first step towards such languages, we executed a study that catalogs how people represent constraint problems graphically. We studied three groups with different expertise: non-computer scientists, computer scientists and constraint programmers and analyzed their marks on paper (e.g., arrows), gestures (e.g., pointing) and the mappings to problem concepts (e.g., containers, sets). We provide foundations to guide future tool designs allowing people to effectively grasp, model and solve problems through visual representations.

Subexponential concurrent constraint programming

In previous works we have shown that linear logic with subexponentials (SELL), a refinement of linear logic, can be used to specify emergent features of concurrent constraint programming (CCP) languages, such as preferences and spatial, epistemic and temporal modalities. In order to do so, we introduced a number of extensions to SELL, such as subexponential quantifiers for the specification of modalities, and more elaborated subexponential structures for the specification of preferences. These results provided clear proof theoretic foundations to existing systems. This paper goes in the opposite direction, answering positively the question: can the proof theory of linear logic with subexponentials contribute to the development of new CCP languages? We propose a CCP language with the following powerful features: 1) computational spaces where agents can tell and ask preferences (soft-constraints); 2) systems where spatial and temporal modalities can be combined; 3) shared spaces for communication that can be dynamically established; and 4) systems that can dynamically create nested spaces. In order to provide the proof theoretic foundations for such a language, we propose a unified logical framework (SELLS⋒) combining the extensions of linear logic with subexponentials mentioned above, and showing that this new framework has interesting proof theoretical properties such as cut-elimination and a sound and complete focused proof system.

A collective artefact design of decision support systems: design science research perspective

Purpose – The knowledge of artefact design in design science research can have an important application in the improvement of decision support systems (DSS) development research. Recent DSS literature has identified a significant need to develop user-centric DSS method for greater relevance with respect to context of use. The purpose of this paper is to develop a collective DSS design artefact as method in a practical industry context. Design/methodology/approach – Under the influence of goal-directed interaction design principles the study outlines the innovative DSS artefact based on design science methodology to deliver a cutting-edge decision support solution, which provides user-centric provisions through the use of design environment and ontology techniques. Findings – The DSS artefact as collective information technology applications through the application of design science knowledge can effectively be designed to meet decision makers’ contextual needs in an agricultural industry context. Research limitations/implications – The study has limitations in that it was developed in a case study context and remains to be fully tested in a real business context. It is also assumed that the domain decisions can be parameterised and represented using a constraint programming language. Practical implications – The paper concludes that the DSS artefact design and this development successfully overcomes some of the limitations of traditional DSS such as low-user uptake, system obsolescence, low returns on investment and a requirement for continual re-engineering effort. Social implications – The design artefact has the potential of increasing user uptake in an industry that has had relevancy problems with past DSS implementation and has experienced associated poor uptake. Originality/value – The design science paradigm provides structural guidance throughout the defined process, helping ensure fidelity both to best industry knowledge and to changing user contexts.

A Secure Non-monotonic Soft Concurrent Constraint Language

We present a fine-grained security model to enforce the access control on the shared constraint store in Concurrent Constraint Programming (CCP) languages. We show the model for a non-monotonic version of Soft CCP (SCCP), that is an extension of CCP where the constraints have a preference level associated with them. Crisp constraints can be modeled in the same framework as well. In the considered non-monotonic soft version (NmSCCP), it is also possible to remove constraints from the store. The language can be used for coordinating agents on a common store of information that represents the set of shared resources. In such scenarios, it is clearly important to enforce the integrity and confidentiality rights on the resources, in order, for instance, to hide part of the information to some agents, or to prevent an agent to consume too many resources. Finally, we present a bisimulation relation to check equivalence between two programs written in this language.

Specifying and solving symbolic and numeric temporal constraints

Representing and solving combinatorial problems, especially those including temporal constraints, using a constraint programming language remains a challenging task. In this paper, we present a tool to assist users in specifying and solving problems under qualitative and quantitative temporal constraints. The tool is based on the TemPro framework that has the ability to manage both numeric and symbolic temporal constraints within a unique model. Our tool provides a generic template that can be specialized to describe a wide variety of temporal constraint applications. Given a problem under temporal constraints, the proposed tool with its friendly graphical user interface first assists the user in the different steps of the problem specification. The graph representation of the temporal constraint problem and its consistent scenarios are then automatically generated and visualized during the solving phase. The user has also the ability to add or remove some constraints and see the effects of these changes on the consistency of the problem.

Studying cancer-cell populations by programmable models of networks

We draw the basic lines for an approach to build mathematical and programmable network models, to be applied in the study of populations of cancer-cells at different stages of disease development. The methodology we propose uses a stochastic Concurrent Constraint Programming language, a flexible stochastic modelling language employed to code networks of agents. It is applied to (and partially motivated by) the study of differently characterized populations of prostate cancer cells. In particular, we prove how our method is suitable to systematically reconstruct and compare different mathematical models of prostate cancer growth—together with interactions with different kinds of hormone therapy—at different levels of refinement. Moreover, we show our technique at work in analysing the nature of noise and in the possible presence of competing mechanisms in the models proposed.

Multi-site scheduling under production and transportation constraints

For the enterprises organised in several distributed production sites, usually, production scheduling models presume either an instantaneous delivery of products or an unlimited number of available vehicles for transporting products. However, the transportation of the intermediate products to the sites is an important activity within the whole process of manufacturing, and the efficient coordination of production and transportation becomes a challenging problem in the actual higher collaborative and competitive environments. This work focuses on the integrated production and transportation scheduling properly managing the resources capacity, material flows and temporal interdependencies between sites. A case-study is reported and the industrial problem under consideration is modelled as a constraint satisfaction problem (CSP). Besides scheduling under resource constraints, the model presented in this paper expands the packing problem to the area of transportation operations scheduling. It is implemented under the constraint programming language CHIP V5. The provided solutions determine values for the various variables associated to the production and transportation operations realised on the whole multi-site, as well as the curves with the profile of the total consumption of resources in time.

Reference Model of Project Prototyping Problem

Reference Model of Project Prototyping ProblemThe paper presents the idea of reference model of project prototyping problem for the projects that are at risk of failure. The hierarchical structure of declarative model connects two fields: functionalities of a typical service enterprise and management system of project execution in the enterprise. The functionalities as separate Constraints Satisfaction Problems (CSP) are described. CSP contains the sets of decision variables, their domains and constraints, which link these variables. The separated problems described as CSP, then in single main CSP are integrated. On the other hand, these problems can decompose into the subproblems concerning the functionalities of different fields. The open structure of model enables to solve the decision problems with different level of specificity. The decision problem can regard a query about the results of proposed decisions as well as the decisions guaranteeing the expected results. A declarative kind of proposed reference model in a natural way allows to implement its in constraint programming languages. The possibility of this approach illustrates an example.

Project scheduling under resource constraints: Application of the cumulative global constraint in a decision support framework

This paper concerns project scheduling under resource constraints. Traditionally, the objective is to find a unique solution that minimizes the project makespan, while respecting the precedence constraints and the resource constraints. This work focuses on developing a model and a decision support framework for industrial application of the cumulative global constraint. For a given project scheduling, the proposed approach allows the generation of different optimal solutions relative to the alternate availability of outsourcing and resources. The objective is to provide a decision-maker an assistance to construct, choose, and define the appropriate scheduling program taking into account the possible capacity resources. The industrial problem under consideration is modeled as a constraint satisfaction problem (CSP). It is implemented under the constraint programming language CHIP V5. The provided solutions determine values for the various variables associated to the tasks realized on each resource, as well as the curves with the profile of the total consumption of resources on time.

Automated Modelling and Solving in Constraint Programming

Constraint programming can be divided very crudely into modeling and solving. Modeling defines the problem, in terms of variables that can take on different values, subject to restrictions (constraints) on which combinations of variables are allowed. Solving finds values for all the variables that simultaneously satisfy all the constraints. However, the impact of constraint programming has been constrained by a lack of "user-friendliness''. Constraint programming has a major "declarative" aspect, in that a problem model can be handed off for solution to a variety of standard solving methods. These methods are embedded in algorithms, libraries, or specialized constraint programming languages. To fully exploit this declarative opportunity however, we must provide more assistance and automation in the modeling process, as well as in the design of application-specific problem solvers. Automated modelling and solving in constraint programming presents a major challenge for the artificial intelligence community. Artificial intelligence, and in particular machine learning, is a natural field in which to explore opportunities for moving more of the burden of constraint programming from the user to the machine. This paper presents technical challenges in the areas of constraint model acquisition, formulation and reformulation, synthesis of filtering algorithms for global constraints, and automated solving. We also present the metrics by which success and progress can be measured.

Towards a Unified Framework for Declarative Structured Communications

We present a unified framework for the declarative analysis of structured communications. By relying on a (timed) concurrent constraint programming language, we show that in addition to the usual operational techniques from process calculi, the analysis of structured communications can elegantly exploit logic-based reasoning techniques. We introduce a declarative interpretation of the language for structured communications proposed by Honda, Vasconcelos, and Kubo. Distinguishing features of our approach are: the possibility of including partial information (constraints) in the session model; the use of explicit time for reasoning about session duration and expiration; a tight correspondence with logic, which formally relates session execution and linear-time temporal logic formulas.

Solver Independent Modelling of Combinatorial and Optimization Problems

Combinatorial optimization problems appear in many real life applications as timetabling, planning and scheduling. However, they are often NP-hard. This means that there is no general and efficient algorithm for solving them. Modern approaches for tackling combinatorial and optimization problems divide the task into two major tasks: modeling and solving. Modelling means finding a proper formulation of the problem while solving means finding the solution of the problem. The most well-known modeling tools are: constraint programming languages, constraint libraries, (mathematical) modelling languages and specification languages. Modelling languages provide the most high-level practical level of modelling for modellers. There are some known solving techniques to tackle such problems of which the most popular ones are: mathematical methods, constraint programming and local search. Each technique has its own advantages and disadvantages and for a given problem it is unclear at the beginning which technique gives us the best result. Current modeling languages are tied to a specific solving technique. In this paper, we show how the modeling language Zinc can automatically map a conceptual model into corresponding low level model suitable for one of the aforementioned solving techniques.

Local consistency for extended CSPs

We extend the framework of Constraint Satisfaction Problems to make it more suitable for/applicable to modern constraint programming languages where both constraint satisfaction and constraint solving have a role. Some rough principles for local consistency conditions in the extended framework are developed, appropriate notions of local consistency are formulated, and relationships between the various consistency conditions are established.

Towards a specification of the ToonTalk language

ToonTalk is a child-oriented programming language whose environment is an animated virtual world, with objects that children can pick up and use as in a game, such as birds, trucks, and robots, providing direct child-oriented metaphors for programming constructs. Actions performed by a programmer's avatar with these objects are both code and coding. ToonTalk is a powerful system, not just a “toy” system: it is based upon concurrent constraint programming languages, and programs written in languages such as Flat Guarded Horn Clauses and Flat Concurrent Prolog can be straight-forwardly constructed in ToonTalk. However, there is not a specification of ToonTalk, for ready implementation in other environments. We propose that the ToonTalk language lies not in the animations displayed by the current environment, but on the actions performed by the programmer with virtual world objects; we present a description and analysis of the methods the ToonTalk language provides to programmers for expressing programs.

A Framework for Timed Concurrent Constraint Programming with External Functions

The timed concurrent constraint programming language (tccp in short) was introduced for modeling reactive systems. This language allows one to model in a very intuitive way typical ingredients of these systems such as timeouts, preemptions, etc. However, there is no natural way for modeling other desirable features such as functional computations, for example for calculating arithmetic results. In fact, although it is certainly possible to implement such kind of operations, each single step of the computation takes time in tccp, and avoiding interferences with the intended overall behavior of the (reactive) system is quite involved.In this paper, we propose an extension of tccp for modeling instantaneous computations which improves the expressiveness of the language, in the sense that operations that are cumbersome to implement in pure tccp, are executed by calling an efficient, external functional engine, while the tccp programmer can focus on the pure, and usually more complex, reactive part of the system. We also describe a case study which motivates the work, and discuss how the new capability presented here can also be used as a new tool for developers from the verification point of view.

Programmed Search in a Timetabling Problem over Finite Domains

Labeling is crucial in the performance of solving timetabling problems with constraint programming. Traditionally, labeling strategies are based on static and dynamic information about variables and their domains, and selecting variables and values to assign. However, the size of combinatorial problems tractable by these techniques is limited. In this paper, we present a real problem solved with constraint programming using programmed search based on the knowledge about the solution structure as a starting point for classical propagation and labeling techniques to find a feasible solution. For those problems in which solutions are close to the seed because of its structure, propagation and labeling can reach a first solution within a small response time. We apply our approach to a real timetabling problem, and we tackle its implementation with two different languages, OPL and TOY, using the constraint programming paradigm over finite domains. While OPL is a commercial, algebraic, and specific-purpose constraint programming language, TOY is a prototype of a general-purpose constraint functional logic programming language. We present the specification of the problem, its implementation with both languages, and a comparative performance analysis.

Introduction Special Issue on Multiparadigm Languages and Constraint Programming

In recent years much research and implementation effort has been devoted both to multiparadigm languages and constraint programming languages. Following up on a series of 11 workshops (WFLP) on mul...

Introduction Special Issue on Multiparadigm Languages and Constraint Programming

In recent years much research and implementation effort has been devoted both to multiparadigm languages and constraint programming languages. Following up on a series of 11 workshops (WFLP) on multiparadigm languages and constraint programming, and as a result of an open call for submissions, the journal on Theory and Practice of Logic Programming is now publishing the results of the selection of the papers submitted to this special issue.