It is well-known that the verification of partial correctness properties of imperative programs can be reduced to the satisfiability problem for constrained Horn clauses (CHCs). However, state-of-the-art solvers for c...
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It is well-known that the verification of partial correctness properties of imperative programs can be reduced to the satisfiability problem for constrained Horn clauses (CHCs). However, state-of-the-art solvers for constrained Horn clauses (or CHC solvers) based on predicate abstraction are sometimes unable to verify satisfiability because they look for models that are definable in a given class A of constraints, called A-definable models. We introduce a transformation technique, called Predicate Pairing, which is able, in many interesting cases, to transform a set of clauses into an equisatisfiable set whose satisfiability can be proved by finding an A-definable model, and hence can be effectively verified by a state-of-the-art CHC solver. In particular, we prove that, under very general conditions on A, the unfold/fold transformation rules preserve the existence of an A-definable model, that is, if the original clauses have an A-definable model, then the transformed clauses have an A-definable model. The converse does not hold in general, and we provide suitable conditions under which the transformed clauses have an A-definable model if and only if the original ones have an A-definable model. Then, we present a strategy, called Predicate Pairing, which guides the application of the transformation rules with the objective of deriving a set of clauses whose satisfiability problem can be solved by looking for A-definable models. The Predicate Pairing (PP) strategy introduces a new predicate defined by the conjunction of two predicates occurring in the original set of clauses, together with a conjunction of constraints. We will show through some examples that an A-definable model may exist for the new predicate even if it does not exist for its defining atomic conjuncts. We will also present some case studies showing that Predicate Pairing plays a crucial role in the verification of relational properties of programs, that is, properties relating two programs (such as
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 organ...
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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 constraintprogramming, logicprogramming, 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.
Dynamical modeling has proven useful for understanding how complex biological processes emerge from the many components and interactions composing genetic regulatory networks (GRNs). However, the development of models...
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Dynamical modeling has proven useful for understanding how complex biological processes emerge from the many components and interactions composing genetic regulatory networks (GRNs). However, the development of models is hampered by large uncertainties in both the network structure and parameter values. To remedy this problem, the models are usually developed through an iterative process based on numerous simulations, confronting model predictions with experimental data and refining the model structure and/or parameter values to repair the inconsistencies. In this paper, we propose an alternative to this gene rate-and-test approach. We present a four-step method for the systematic construction and analysis of discrete models of GRNs by means of a declarative approach. Instead of instantiating the models as in classical modeling approaches, the biological knowledge on the network structure and its dynamics is formulated in the form of constraints. The compatibility of the network structure with the constraints is queried and in case of inconsistencies, some constraints are relaxed. Common properties of the consistent models are then analyzed by means of dedicated languages. Two such languages are introduced in the paper. Removing questionable constraints or adding interesting ones allows to further analyze the models. This approach allows to identify the best experiments to be carried out, in order to discriminate sets of consistent models and refine our knowledge on the system functioning. We test the feasibility of our approach, by applying it to the re-examination of a model describing the nutritional stress response in the bacterium Escherichia coli. (C) 2009 Elsevier Ireland Ltd. All rights reserved.
Teachers' absences are a common disruption to the provision of academic courses. They make it necessary to modify teacher assignment, which amounts to finding suitable substitutions. Sometimes it happens that the ...
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We present a novel approach to deciding the validity of formulas in first-order fixpoint logic with background theories and arbitrarily nested inductive and co-inductive predicates defining least and greatest fixpoint...
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We present a novel approach to deciding the validity of formulas in first-order fixpoint logic with background theories and arbitrarily nested inductive and co-inductive predicates defining least and greatest fixpoints. Our approach is constraint-based, and reduces the validity checking problem of the given first-order-fixpoint logic formula (formally, an instance in a language called mu CLP) to a constraint satisfaction problem for a recently introduced predicate constraint language. Coupled with an existing sound-and-relatively-complete solver for the constraint language, this novel reduction alone already gives a sound and relatively complete method for deciding mu CLP validity, but we further improve it to a novel modular primal-dual method. The key observations are (1) mu CLP is closed under complement such that each (co-)inductive predicate in the original primal instance has a corresponding (co-)inductive predicate representing its complement in the dual instance obtained by taking the standard De Morgans dual of the primal instance, and (2) partial solutions for (co-)inductive predicates synthesized during the constraint solving process of the primal side can be used as sound upper-bounds of the corresponding (co-)inductive predicates in the dual side, and vice versa. By solving the primal and dual problems in parallel and exchanging each others partial solutions as sound bounds, the two processes mutually reduce each others solution spaces, thus enabling rapid convergence. The approach is also modular in that the bounds are synthesized and exchanged at granularity of individual (co-)inductive predicates. We demonstrate the utility of our novel fixpoint logic solving by encoding a wide variety of temporal verification problems in mu CLP, including termination/non-termination, LTL, CTL, and even the full modal mu-calculus model checking of infinite state programs. The encodings exploit the modularity in both the program and the property by expressing each loo
This paper presents a new approach for the prediction of substructures in building facades based on sparse observations. We automatically generate a small number of most likely hypotheses and provide probabilities for...
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This paper presents a new approach for the prediction of substructures in building facades based on sparse observations. We automatically generate a small number of most likely hypotheses and provide probabilities for each of them. Probability density functions of model parameters which in most cases are non Gaussian and multimodal are learned from training data and approximated by Gaussian mixtures. Relations between model parameters are represented by non-linear constraints. For stochastic reasoning we design and apply a special kind of Bayesian networks which involves both discrete as well as continuous variables, a scenario which often suggests the use of approximate inference which however is infeasible in the face of a huge number of competing model hypotheses. In order to be able to scan huge model spaces avoiding the pitfalls of approximate reasoning and to exploit the potential of both observations and models, we combined Bayesian networks with constraintlogic programs. We designed a method which breaks down the problem into a feasible number of subproblems for which exact inference can be applied. We illustrate our approach with building facades and demonstrate that particularly for buildings with strong symmetries number and position of windows can be deduced on the basis of ground plans alone. (C) 2015 Elsevier Ltd. All rights reserved.
To overcome inefficiency in traditional logicprogramming, a declarative programming language COPS is designed based on the notion of concurrent constraintprogramming (CCP). The improvement is achieved by the adoptio...
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To overcome inefficiency in traditional logicprogramming, a declarative programming language COPS is designed based on the notion of concurrent constraintprogramming (CCP). The improvement is achieved by the adoption of constraint-based heuristic strategy and the introduction of deterministic components in the framework of CCP. Syntax specification and an operational semantic description are presented.
Uncertainty in logicprogramming has been investigated during the last decades, dealing with various extensions of the classical LP paradigm and different applications. Existing proposals rely on different approaches,...
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Uncertainty in logicprogramming has been investigated during the last decades, dealing with various extensions of the classical LP paradigm and different applications. Existing proposals rely on different approaches, such as clause annotations based on uncertain truth values, qualification values as a generalization of uncertain truth values, and unification based on proximity relations. On the other hand, the CLP scheme has established itself as a powerful extension of LP that supports efficient computation over specialized domains while keeping a clean declarative semantics. In this paper we propose a new scheme SQCLP designed as an extension of CLP that supports qualification values and proximity relations. We show that several previous proposals can be viewed as particular cases of the new scheme, obtained by partial instantiation. We present a declarative semantics for SQCLP that is based on observables, providing fixpoint and proof-theoretical characterizations of least program models as well as an implementation-independent notion of goal solutions.
Computer Supported Collaborative Learning scripts define pedagogically effective practices for organizing collaborative activities. This paper presents a novel platform for defining CSCL scripts. This platform is comp...
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ISBN:
(纸本)9783030505059;9783030505066
Computer Supported Collaborative Learning scripts define pedagogically effective practices for organizing collaborative activities. This paper presents a novel platform for defining CSCL scripts. This platform is composed of the following components: a) a formal language named COSTLy for the specification of CSCL scripts based on logic and constraints, b) a visual environment that facilitates the authoring of scripts, based on the formal language, and c) a mechanism that translates the abstract definitions of scripts into constraintlogic programs, thus implementing group formation and task distribution of actual scenario instances. The expressiveness of the proposed language was evaluated. Also the results of a usability evaluation of the proposed platform are reported in the paper. It was shown that university students were able to use the platform in order to describe CSCL scripts of high complexity.
This study deals with decision support system and optimization of parallel handling of groups of jobs. All jobs in a group should be delivered at the same time after processing. The authors present a novel hybrid appr...
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