A Tool Support for Reusing ELAN Rule-Based Components
A. Martins Moreia (Universidade Federal do Rio Grande do Norte, Natal, Brazil)
C. Ringeissen (LORIA-INRIA, France)
A. Santana (Universidade Federal do Rio Grande do Norte, Natal, Brazil)

OnDemandOBJ: A Laboratory for Strategy Annotations
M. Alpuente (Universidad Politécnica de Valencia, Spain)
S. Escobar (Universidad Politécnica de Valencia, Spain)
S. Lucas (Universidad Politécnica de Valencia, Spain)

Strategy annotations are used in rule-based programming languages such as OBJ2, OBJ3, CafeOBJ, and Maude to improve efficiency and/or reduce the risk of nontermination. Syntactically, they are given either as lists of natural numbers or as lists of integers associated to function symbols whose (absolute) values refer to the arguments of the corresponding symbol. A positive index enables the evaluation of an argument whereas a negative index means ``evaluate on-demand''. In this paper, we present OnDemandOBJ, an implementation of strategy-guided on-demand evaluation, which improves previous mechanizations that were lacking satisfactory computational properties.

On-Demand evaluation by program transformation
M. Alpuente (Universidad Politécnica de Valencia, Spain)
S. Escobar (Universidad Politécnica de Valencia, Spain)
S. Lucas (Universidad Politécnica de Valencia, Spain)

Strategy annotations are used in eager programming languages (e.g., OBJ2, OBJ3, CafeOBJ, and Maude) for improving efficiency and/or reducing the risk of nontermination. Syntactically, they are given either as lists of natural numbers or as lists of integers associated to function symbols whose (absolute) values refer to the arguments of the corresponding symbol. A positive index enables the evaluation of an argument whereas a negative index means ``evaluation on-demand''. Recently, we have introduced a formal description of the operational meaning of such on-demand strategy annotations which improves previous formalizations that were lacking satisfactory computational properties. In this paper, we introduce an automatic, semantics--preserving program transformation which produces a program (without negative annotations) which can be then correctly executed by typical OBJ interpreters. Moreover, to demonstrate the practicality of our ideas, the program transformation has been implemented (in Haskell) and we compare the evaluation of transformed programs with the original ones on a set of representative benchmarks.

Domain-Specific Optimisation with User-Defined Rules in CodeBoost
O.S. Bagge (University of Bergen, Norway)
M. Haveraaen (University of Bergen, Norway)

Translating Combinatory Reduction Systems into the Rewriting Calculus
C. Bertolissi (LORIA-INPL, France)
H. Cirstea (LORIA-Nancy 2, France)
C. Kirchner (LORIA-INRIA, France)

Deductive Generation of Constraint Propagation Rules
S. Brand (CWI, Amsterdam, The Netherlands)
E. Monfroy (Université de Nantes, France)

Constraint propagation can often be conveniently captured in a rule-based paradigm. In recent years, a number of techniques for automatic generation of rule-based constraint solvers have been described. Most of them use the generate-and-test principle. We present here a rule-generation method that is based on deduction. A solver for a simple constraint represented by a rule set is transformed into a new solver for a complex constraint, with a guarantee on the associated consistency notion. We describe deductive incremental constructions of constraint solvers for several types of constraint definitions, among them conjunction, universal and existential quantification, and restriction.

Typing rule-based transformations over topological collections
J. Cohen (LaMI-CNRS, Évry, France)

Pattern-matching programming is an example of a rule-based programming style developed in functional languages. This programming style is intensively used in dialects of ML but is restricted to algebraic data-types. This restriction limits the application domain. However, as shown by Giavitto and Michel at RULE'02, case-based function definitions can be extended to more general data structures called topological collections. We show in this paper that this extension retains the benefits of the typed discipline of the functional languages. More precisely, we show that topological collections and the rule-based definition of functions associated with them fit in a polytypic extension of mini-ML where type inference is still possible.

Design and Implementation of the L+C Modeling Language
R. Karwowski (University of Calgary, Canada)
P. Prusinkiewicz (University of Calgary, Canada)