作者：
Roventa, EYork Univ
Glendon Coll Dept Comp Sci Toronto ON M4N 3M6 Canada
This paper presents an enhancement of the CARESS system-A constraint Approximative Reasoning System Support-introduced in (Popescu and Roventa, 1994). CARESS is an experimental system with primarily two objectives: (1...
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This paper presents an enhancement of the CARESS system-A constraint Approximative Reasoning System Support-introduced in (Popescu and Roventa, 1994). CARESS is an experimental system with primarily two objectives: (1) to study fuzzy knowledge representation and manipulation techniques and to implement them in PROLOG III, and (2) to develop a knowledge programming environment for building expert systems. We discuss here the use of meta-programming, constraint logic programming and approximate reasoning for the design of expert systems. It has already been proven that meta-programming and logicprogramming are powerful techniques for expert system design. Fuzzy logic can be used to model one kind of uncertainty. constraint logic programming is useful for dealing with the constraints given by operations using fuzzy sets.
The embedding of constraint satisfaction on the domain of discourse into a rule-based programming paradigm like logicprogramming provides a powerful reasoning tool. We present an application in spatial reasoning that...
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The embedding of constraint satisfaction on the domain of discourse into a rule-based programming paradigm like logicprogramming provides a powerful reasoning tool. We present an application in spatial reasoning that uses this combination to produce a clear, concise, yet very expressive system through its ability to manipulate partial information. Three-dimensional solid objects in constructive solid geometry representation are manipulated, and their spatial relationship with one another, points, or regions is reasoned about. The language used to develop this application is QUAD-CLP(R), an experimental constraint logic programming language of our own design, which is equipped with a solver for quadratic and linear arithmetic constraints over the reals.
This paper represents an integration of Mixed Integer programming (MIP) and constraint logic programming (CLP) which, like MIP, tightens bounds rather than adding constraints during search. The integrated system combi...
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This paper represents an integration of Mixed Integer programming (MIP) and constraint logic programming (CLP) which, like MIP, tightens bounds rather than adding constraints during search. The integrated system combines components of the CLP system ECLiPSe [7] and the MIP system CPLEX [5], in which constraints can be handled by either one or both components. Our approach is introduced in three stages. Firstly, we present an automatic transformation which maps CLP programs onto such CLP programs that any disjunction is eliminated in favour of auxiliary binary variables. Secondly, we present improvements of this mapping by using a committed choice operator and translations of pre-defined non-linear constraints. Thirdly, we introduce a new hybrid algorithm which reduces the solution space of the problem progressively by calling finite domain propagation of ECLiPSe as well as dual simplex of CPLEX. The advantages of this integration are illustrated by efficiently solving difficult optimisation problems like the Hoist Scheduling Problem [23] and the Progressive Party Problem [27].
The complementing strengths of constraint (logic) programming (CLP) and Mixed Integer programming (IP) have recently received significant attention. Although various optimization and constraintprogramming packages at...
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The complementing strengths of constraint (logic) programming (CLP) and Mixed Integer programming (IP) have recently received significant attention. Although various optimization and constraintprogramming packages at a first glance seem to support mixed models, the modeling and solution techniques encapsulated are still rudimentary. Apart from exchanging bounds for variables and objective, little is known of what constitutes a good hybrid model and how a hybrid solver can utilize the complementary strengths of inference and relaxations. This paper adds to the field by identifying constraints as the essential link between CLP and IP and introduces an algorithm for bidirectional inference through these constraints. Together with new search strategies for hybrid solvers and cut-generating mixed global constraints, solution speed is improved over both traditional IP codes and newer mixed solvers.
In hybrid solvers for combinatorial optimisation, combining constraint (logic) programming (CLP) and Mixed Integer programming (MIP), it is important to have tight connections between the two domains. We extend and ge...
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In hybrid solvers for combinatorial optimisation, combining constraint (logic) programming (CLP) and Mixed Integer programming (MIP), it is important to have tight connections between the two domains. We extend and generalise previous work on automatic linearisations and propagation of symbolic CLP constraints that cross the boundary between CLP and MIP. We also present how reduced costs from the linear programming relaxation can be used for domain reduction on the CLP side. Computational results comparing our hybrid approach with pure CLP and MIP on a configuration problem show significant speed-ups.
In this paper, we investigate the possibility of integrating Artificial Intelligence (AI) and Operations Research (OR) techniques for solving the Crew Rostering Problem (CRP). CRP calls for the optimal sequencing of a...
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Decision support systems provide decision-makers with an interactive environment for analyses of information with various models to help solve unstructured problems. constraint logic programming as an improvement of l...
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Decision support systems provide decision-makers with an interactive environment for analyses of information with various models to help solve unstructured problems. constraint logic programming as an improvement of logicprogramming can be used as a tool for the development of such decision support systems. constraint logic programming is an integrated paradigm of logic modelling and mathematical programming. It has a modelling and analysis capacity for problems containing both qualitative and quantitative constraints;it has well-established declarative and procedural semantics, which reduce the model builder's burden to specify problem solving procedures as a part of a model. In this paper, we demonstrate the use of constraint logic programming as a potential decision support system tool, focusing on the model representation and analysis aspects. (C) 1998 Elsevier Science B.V.
This paper presents a new method for design space exploration for distributed embedded systems. The method is based on constraint logic programming (CLP) and make it possible to model distributed embedded systems and ...
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This paper presents a new method for design space exploration for distributed embedded systems. The method is based on constraint logic programming (CLP) and make it possible to model distributed embedded systems and design requirements using finite domain constraints. Design space exploration tools can then use this model to find different solutions satisfying constraints. An advantage of this method is its flexibility to defined design constraints and ability to mix both manual design decisions and automatic optimization methods. The solution of the set of constraints provides a final system implementation which, by definition, satisfies all imposed design constraints. Both algorithms guaranteeing optimal solutions and heuristic methods can be used in the optimization phase. (C) 2001 Elsevier Science B.V. All rights reserved.
We present a prescriptive type system with parametric polymorphism and subtyping for constraintlogic programs. The aim of this type system is to detect programming errors statically. It introduces a type discipline f...
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We present a prescriptive type system with parametric polymorphism and subtyping for constraintlogic programs. The aim of this type system is to detect programming errors statically. It introduces a type discipline for constraintlogic programs and modules, while maintaining the capabilities of performing the usual coercions between constraint domains. and of typing meta-programming predicates, thanks to the flexibility of subtyping. The property of subject reduction expresses the consistency of a prescriptive type system w.r.t. the execution model: if a program is 'well-typed'. then all derivations starting from a 'well-typed' goat are again 'well-typed'. That property is proved w.r.t. the abstract execution model of constraintprogramming which proceeds by accumulation of constraints only. and w.r.t. an enriched execution model with type constraints for substitutions. We describe our implementation of the system for type checking and type inference. We report our experimental results on type checking ISO-Prolog, the (constraint) libraries of Sicstus Prolog and other Prolog programs.
In this paper, we investigate the possibility of integrating Artificial Intelligence (AI) and Operations Research (OR) techniques for solving the Crew Rostering Problem (CRP). CRP calls for the optimal sequencing of a...
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In this paper, we investigate the possibility of integrating Artificial Intelligence (AI) and Operations Research (OR) techniques for solving the Crew Rostering Problem (CRP). CRP calls for the optimal sequencing of a given set of duties into rosters satisfying a set of constraints. The optimality criterion requires the minimization of the number of crews needed to cover the duties. This kind of problem has been traditionally solved by OR techniques. In recent years, a new programming paradigm based on logicprogramming, named constraint logic programming (CLP), has been successfully used for solving hard combinatorial optimization problems. CLP maintains all the advantages of logicprogramming such as declarativeness, non-determinism and an incremental style of programming, while overcoming its limitations, mainly due to the inefficiency in exploring the search space, CLP achieves good results on hard combinatorial optimization problems which, however, are not comparable with those achieved by OR approaches, Therefore, we integrate both techniques in order to design an effective heuristic algorithm for CRP which fully exploits the advantages of the two methodologies: on the one hand, we maintain the declarativeness of CLP, its ease of representing knowledge and its rapid prototyping;on the other hand, we inherit from OR some efficient procedures based on a mathematical approach to the problem, Finally, we compare the results we achieved by means of the integration with those obtained by a pure OR approach, showing that AP and OR techniques for hard combinatorial optimization problems can be effectively integrated, (C) 1998 by John Wiley & Sons, Ltd.
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