Constraint Logic Programming Research Articles

Reasoning about UML/OCL class diagrams using constraint logic programming and formula

Model Driven Engineering promotes the use of models as the main artifacts in software and system development. Verification and validation of models are key activities to ensure the quality of the system under development. This paper presents a framework to reason about the satisfiability of class models described using the Unified Modeling Language (UML). The proposed framework allows us to identify possible design flaws as early as possible in the software development cycle. More specifically, we focus on UML Class Diagrams annotated with Object Constraint Language (OCL) invariants, which are considered to be the main artifacts in Object-Oriented analysis and design for representing the static structure of a system. We use the Constraint Logic programming (CLP) paradigm to reason about UML Class Diagrams modeling foundations. In particular, we use Formula as a model-finding and design space exploration tool. We also present an experimental Eclipse plug-in, which implements our UML model to Formula translation proposal following a Model Driven Architecture (MDA) approach. The proposed framework can be used to reason, validate, and verify UML Class Diagram software designs by checking correctness properties and generating model instances using the model exploration tool Formula.

Constraint Logic Programming with Polynomial Constraints over Finite Domains

Constraint Logic Programming with Polynomial Constraints over Finite Domains

Computation and comparison of nonmonotonic skeptical inference relations induced by sets of ranking models for the realization of intelligent agents

Skeptical inference of an intelligent agent in the context of a knowledge base $\mathcal {R}$ containing conditionals of the form If A then usually B can be defined with respect to a set of models of $\mathcal {R}$ . For the semantics of ranking functions that assign a degree of surprise to each possible world, we develop a method for comparing the inference relations induced by different sets of ranking models. Using this method, we address the problem of ensuring the correctness of approximating skeptical c-inference for $\mathcal {R}$ by constraint satisfaction problems (CSPs) over finite domains. Skeptical c-inference is defined by taking the set of all c-representations into account, where c-representations are ranking functions induced by impact vectors encoding the conditional impact on each possible world. By setting a bound for the maximal impact value, c-inference can be approximated by a resource-bounded inference operation. We investigate the concepts of regular and sufficient upper bounds for conditional impacts and how they can be employed for implementing c-inference as a finite domain constraint solving problem. While in general, determining a sufficient upper bound for these CSPs is an open problem, for a sequence of simple knowledge bases investigated only experimentally before, we prove that using the number of conditionals in $\mathcal {R}$ as an upper bound correctly captures skeptical c-inference. The ideas presented in this paper are implemented in a software platform that realizes the core reasoning component of an intelligent agent.

Freight transportation broker agent based on constraint logic programming

We propose an agent-based freight brokering system that provides an intelligent logistics brokerage service focusing on the transport activity for the efficient allocation of transport resources (vehicles or trucks) to the transport applications. The freight broker has the role to coordinate transportation arrangements of transport customers with transport resource providers. In this paper we focus on the fundamental function of this business that aims to find available trucks and to define their feasible routes for transporting requested customer loads. Our main achievement is the proposal of a knowledge-based freight broker based on agents and constraint programming. We describe the multi-agent architecture and the interaction protocol of the freight broker. The brokering function is defined as a special type of vehicle routing with pickup and delivery problem. This function is achieved by proposing an optimization agent based on constraint logic programming. We present in detail our novel declarative optimization model, as well as the components of the optimization agent. This model was implemented and evaluated using the state-of-the-art ECLiPSe-CLP engine. The experimental results that we obtained for sample benchmark problems are promising and they support the effectiveness of our approach in a practical setting.

Performing fully parallel constraint logic programming on a quantum annealer

AbstractAquantum annealerexploits quantum effects to solve a particular type of optimization problem. The advantage of this specialized hardware is that it effectively considers all possible solutions in parallel, thereby potentially outperforming classical computing systems. However, despite quantum annealers having recently become commercially available, there are relatively few high-level programming models that target these devices. In this article, we show how to compile a subset of Prolog enhanced with support for constraint logic programming into a two-local Ising-model Hamiltonian suitable for execution on a quantum annealer. In particular, we describe the series of transformations one can apply to convert constraint logic programs expressed in Prolog into an executable form that bears virtually no resemblance to a classical machine model yet that evaluates the specified constraints in a fully parallel manner. We evaluate our efforts on a 1,095-qubit D-Wave 2X quantum annealer and describe the approach's associated capabilities and shortcomings.

Dynamic modeling of planar closed-chain robotic manipulators in flight and impact phases

This paper presents a systematic procedure for the dynamic modeling of a closed-chain robotic system in both the flight and impact phases. In the proposed approach, first, a planar closed-chain system constructed of n rigid links and n+1 revolute joints is virtually converted to an open kinematic chain. Then, a recursive algorithm based on the Gibbs–Appell principle is applied to reduce the computational load of the developed model, which contains finite constraints. The normal impact phase of this closed-chain robotic system, which includes impulsive constraints, is modeled based on the Newton's kinematic impact law. Finally, computer simulations of a hexagonal closed-chain robotic system in both the flight and impact phases are carried out. This work is actually an extension of the previous investigation of the authors, which was restricted to the study of an open kinematic chain. So, in order to avoid duplication, only the necessary modifications in converting open kinematic chains to closed-chain robotic manipulators are presented here. To the best of our knowledge, this is the first time that a combination of finite and impulsive constraints in a closed-chain mechanical system has been recursively formulated. Since experimental verification of the proposed model is not easy to achieve in this case and no well-defined benchmark problems could be found to validate the model, the findings of this work are compared with the results obtained by simulating the same system in the “Working Model” computer software.

Towards a characterization of constant-factor approximable finite-valued CSPs

We study the approximability of (Finite-)Valued Constraint Satisfaction Problems (VCSPs) with a fixed finite constraint language Γ consisting of finitary functions on a fixed finite domain. Ene et al. have shown that, under a mild technical condition, the basic LP relaxation is optimal for constant-factor approximation for VCSP(Γ) unless the Unique Games Conjecture fails. Using the algebraic approach to the CSP, we give new natural algebraic conditions for the finiteness of the integrality gap for the basic LP relaxation of VCSP(Γ) and show how this leads to efficient constant-factor approximation algorithms for several examples that cover all previously known cases that are NP-hard to solve to optimality but admit constant-factor approximation. Finally, we show that the absence of another algebraic condition leads to NP-hardness of constant-factor approximation. Thus, our results indicate where the boundary of constant-factor approximability for VCSPs lies.

Assessing SMT and CLP approaches for workflow nets verification

In the actual business world, companies rely more and more on workflows to model the core of their business processes. In this context, the focus of workflow analysts is made on the verification of workflows specifications, in particular of modal specifications that allow the description of necessary or admissible behaviors. The design and the analysis of business processes commonly relies on workflow nets, a suited class of Petri nets. The goal of this paper is to evaluate and compare in a deep way two resolution methods—satisfiability modulo theory and constraint logic programming—applied to the verification of modal specifications over workflow nets. Firstly, it provides a concise description of the verification methods based on constraint solving. Secondly, it introduces the toolchain developed to automate the full verification process. Thirdly, it describes the experimental protocol designed to evaluate and compare the scalability and efficiency of both resolution approaches and reports on the obtained results. Finally, these obtained results are discussed in detail, lessons learned from these experiments are given, and, on the basis of experiments feedback, directions for improvement and future work are suggested.

Secure transmission for multi-antenna wireless powered communication with co-channel interference and self-energy recycling

In this paper, we investigate secure transmission resource allocation problem for multi-antenna wireless powered communication networks (WPCNs) where a full-duplex (FD) wireless device (WD) sends confidential signals to an information receiver (IR), and an energy transmitter (ET) is employed to provide radio energy to the FD WD and to jam the multiple eavesdroppers at the same time. In order to improve energy efficiency, the FD WD also collects energy from its own loop interference signals. The energy FD WD harvested is supplied to its information transmission in the same time slot. We aim to maximize the achievable secrecy rate at the IR by designing the transmit covariances of the WD and ET under the energy harvesting constraint of WD and transmit power constraints of WD and ET, respectively. Firstly, we consider perfect channel state information (CSI) of all links. Due to the non-convexity of the optimization problem, we propose a two-level optimization algorithm, the inner level optimization problem is a semi-definite programming (SDP) problem and the outer level optimization problem is a single-variable optimization problem. The optimal solution is derived and the sufficient condition to ensure that transmit beamforming having rank-one structure is also provided. Then, we consider robust secure transmit scheme design where the CSI uncertainties are molded by the worst-case model, and S-procedure and its extension are employed to transform the semi-infinite constraint problem into finite constraint problem. Similarly, the optimal robust solution and the sufficient condition to ensure that robust transmit beamforming is optimal are provided. Simulation results demonstrate the effectiveness of our proposed strategies.

Constraint-Based Refactoring

Constraint-based refactoring generalizes constraint-based type refactoring as introduced by Tip et al. [61] by extending the coverage of change from types to names, locations, accessibilities, and other properties of program elements. Starting with a generic specification of refactoring tools, we systematically develop constraint-based refactoring as a generic solution to a certain class of refactoring problems and provide a condition under which constraint-based refactoring tools are proven correct for any given target language. Although compliance with this correctness condition is hard to prove for target languages whose semantics is not formally defined, we show how the condition gives rise to automated testing procedures. We present a novel algorithm based on constraint-logic programming for the generation of constraints from a program to be refactored, and demonstrate its time and space requirements by using it in the application of refactorings to open source programs. Summarizing earlier work, we show how the principles underlying constraint-based refactoring tools extend to ad hoc refactoring, cross-language refactoring, and model/code co-refactoring.

Application of CLP for technical objects construction with an exemplary bucket elevation

The paper presents the application of the Constraint Logic Programming (CLP) language for designing technical objects. The method was described and illustrated based with the help of on the bucket elevator design. The hierarchical structure of the object, way of system configuration and exemplary sets of fitted components are presented.

Route Optimization of Electric Vehicles Based on Dynamic Wireless Charging

One of the barriers for the adoption of electric vehicles (EVs) is the anxiety around the limited driving range. Recent proposals have explored charging EVs on the move, using dynamic wireless charging which enables power exchange between the vehicle and the grid while the vehicle is moving. In this paper, we focus on the intelligent routing of EVs in need of charging so that they can make most efficient use of the so-called mobile energy disseminators (MEDs) which operate as mobile charging stations. We present a method for routing EVs around MEDs on the road network, which is based on constraint logic programming and optimization using a graph-based shortest path algorithm. The proposed method exploits inter-vehicle communications in order to eco-route electric vehicles. We argue that combining modern communications between vehicles and state of the art technologies on energy transfer, the driving range of EVs can be extended without the need for larger batteries or overtly costly infrastructure. We present extensive simulations in city conditions that show the driving range and consequently the overall travel time of electric vehicles is improved with intelligent routing in the presence of MEDs.

A multi-level approach to ubiquitous modeling and solving constraints in combinatorial optimization problems in production and distribution

Constraints, although ubiquitous in production and distribution planning, scheduling and control, often lead to inconsistencies in the decision-making process. The constraint-based modeling helps circumvent many organization-impacting issues. To address this, we developed a multi-level approach to the modeling and solving of combinatorial optimization problems. It is versatile and effective owing to the use of multi-level presolving and multiple paradigms, such as constraint programming, logic programming, mathematical programming and fuzzy logic, for their complementary strengths. The capability of this framework and its advantage over mathematical programming alone or over hybrid frameworks is shown in the illustrative example, in which combinatorial optimization is used as a benchmark to prove the effectiveness of the proposed approach. Knowledge of the problem is stored in the form of facts.

Capacitated vehicle routing problem with pick-up and alternative delivery (CVRPPAD): model and implementation using hybrid approach

The paper presents an optimization model and its implementation using a hybrid approach for the Capacitated Vehicle Routing Problem with Pick-up and Alternative Delivery (CVRPPAD). The development of the CVRPPAD was motivated by postal items distribution issues. The model proposed combines various features of Vehicle Routing Problem variants. The novelty of this model lies in the introduction of alternativeness of item delivery points, differentiation of delivery point types in terms of their capacity (parcel lockers) and possibility of collecting the items during the execution of delivery routes. The model also takes into account a possibility of introducing time windows related to delivery time. The proposed data structure in the form of sets of facts facilitates the implementation of the model in all environments, Constraint Logic Programming (CLP), Mathematical Programming (MP), metaheuristics, databases, etc. The model is implemented in the MP, hybrid CLP/MP, and hybrid CLP/heuristic environments. The hybrid CLP/MP approach is the authors’ original solution, which has already been used to solve Supply Chain Management problems, scheduling problems, routing problems etc. For large size problems, considering their combinatorial character, the proposed CLP/MP approach is ineffective. Its effectiveness will improve when MP is replaced by a heuristic. All implementations were performed on the same data instances (facts), which made it possible to compare them according to the solution search time, number of decision variables and number of constraints. The directions into which the model can be further developed were also presented.

Kripke semantics for higher-order type theory applied to constraint logic programming languages

We define a Kripke semantics for Intuitionistic Higher-Order Logic with constraints formulated within Church's Theory of Types via the addition of a new constraint base type. We then define an executable fragment, hoHH(C), of the logic: a higher-order logic programming language with typed λ-abstraction, implication and universal quantification in goals and constraints, and give a modified model theory for this fragment. Both formal systems are shown sound and complete for their respective semantics.We also solve the impredicativity problem in λProlog semantics, namely how to give a definition of truth without appealing to induction on subformula structure. In the last section we give a simple semantics-based conservative extension proof that the language hoHH(C) satisfies a uniformity property along the lines of [39].

A constraint-driven approach to food supply chain management

PurposeThe purpose of this paper is the need to build a novel approach that would allow flexible modeling and solving of food supply chain management (FSCM) problems. The models developed would use the data (data-driven modeling) as early as possible at the modeling phase, which would lead to a better and more realistic representation of the problems being modeled.Design/methodology/approachAn essential feature of the presented approach is its declarativeness. The use of a declarative approach that additionally includes constraint satisfaction problems and provides an opportunity of fast and easy modeling of constrains different in type and character. Implementation of the proposed approach was performed with the use of an original hybrid method in which constraint logic programming (CLP) and mathematical programming (MP) are integrated and transformation of a model is used as a presolving technique.FindingsThe proposed constraint-driven approach has proved to be extremely flexible and efficient. The findings obtained during part of experiments dedicated to efficiency were very interesting. The use of the constraint-driven approach has enabled finding a solution depending on the instance data up to 1,000 times faster than using the MP.Research limitations/implicationsDue to the limited use of exact methods for NP-hard problems, the future study should be to integrate the CLP with environments other than the MP. It is also possible, e.g., with metaheuristics like genetic algorithms, ant colony optimization, etc.Practical implicationsThere is a possibility of using the approach as a basis to build a decision support system for FSCM, simple integration with databases, enterprise resource planning systems, management information systems, etc.Originality/valueThe new constraint-driven approach to FSCM has been proposed. The proposed approach is an extension of the hybrid approach. Also, a new decision-making model of distribution and logistics for the food supply chain is built. A presolving technique for this model has been presented.

A unified approach to solving seven programming problems (functional pearl)

We present seven programming challenges in Racket, and an elegant, unified approach to solving them using constraint logic programming in miniKanren.

Logic programming approaches for routing fault-free and maximally parallel wavelength-routed optical networks-on-chip (Application paper)

AbstractOne promising trend in digital system integration consists of boosting on-chip communication performance by means of silicon photonics, thus materializing the so-called Optical Networks-on-Chip. Among them, wavelength routing can be used to route a signal to destination by univocally associating a routing path to the wavelength of the optical carrier. Such wavelengths should be chosen so to minimize interferences among optical channels and to avoid routing faults. As a result, physical parameter selection of such networks requires the solution of complex constrained optimization problems. In previous work, published in the proceedings of the International Conference on Computer-Aided Design, we proposed and solved the problem of computing the maximum parallelism obtainable in the communication between any two endpoints while avoiding misrouting of optical signals. The underlying technology, only quickly mentioned in that paper, is Answer Set Programming. In this work, we detail the Answer Set Programming approach we used to solve such problem.Another important design issue is to select the wavelengths of optical carriers such that they are spread across the available spectrum, in order to reduce the likelihood that, due to imperfections in the manufacturing process, unintended routing faults arise. We show how to address such problem in Constraint Logic Programming on Finite Domains.

Verification of Programs via Intermediate Interpretation

We explore an approach to verification of programs via program transformation applied to an interpreter of a programming language. A specialization technique known as Turchin's supercompilation is used to specialize some interpreters with respect to the program models. We show that several safety properties of functional programs modeling a class of cache coherence protocols can be proved by a supercompiler and compare the results with our earlier work on direct verification via supercompilation not using intermediate interpretation. Our approach was in part inspired by an earlier work by E. De Angelis et al. (2014-2015) where verification via program transformation and intermediate interpretation was studied in the context of specialization of constraint logic programs.

Declarative Reasoning about Space and Motion with Video

We present a commonsense theory of space and motion for representing and reasoning about motion patterns in video data, to perform declarative (deep) semantic interpretation of visuo-spatial sensor data, e.g., coming from object tracking, eye tracking data, movement trajectories. The theory has been implemented within constraint logic programming to support integration into large scale AI projects. The theory is domain independent and has been applied in a range of domains, in which the capability to semantically interpret motion in visuo-spatial data is central. In this paper, we demonstrate its capabilities in the context of cognitive film studies for analysing visual perception of spectators by integrating the visual structure of a scene and spectators gaze acquired from eye tracking experiments.