By Jean-Pierre Jouannaud, Ralf Treinen (auth.), Gerhard Goos, Juris Hartmanis, Jan van Leeuwen, Hubert Comon, Claude Marché, Ralf Treinen (eds.)
Constraints offer a declarative means of representing countless units of information. they're well matched for combining various logical or programming paradigms as has been identified for constraint good judgment programming because the Nineteen Eighties and extra lately for practical programming. using constraints in computerized deduction is more moderen and has proved to be very winning, relocating the regulate from the meta-level to the limitations, that are now firstclass objects.
This monograph-like publication offers six completely reviewed and revised lectures given via prime researchers on the summer time institution prepared by way of the ESPRIT CCL operating crew in Gif-sur-Yvette, France, in September 1999. The booklet bargains coherently written chapters on constraints and constraint fixing, constraint fixing on phrases, combining constraint fixing, constraints and theorem proving, practical and constraint common sense programming, and construction business applications.
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Additional resources for Constraints in Computational Logics: Theory and Applications International Summer School, CCL ’99 Gif-sur-Yvette, France, September 5–8, 1999 Revised Lectures
Mazurkiewicz and J´ ozef Winkowski, editors, Concurrency Theory, volume 1243 of Lecture Notes in Computer Science, pages 213–227, Warsaw, Poland, July 1997. Springer-Verlag. Laurent Fribourg and Morcos Veloso Peixoto. Automates concurrents ` a contraintes. Technique et Science Informatiques, 13(6), 1994. Pascal Van Hentenryck. Constraint Satisfaction in Logic Programming. MIT Press, 1989. Nevin Heintze and Joxan Jaffar. A finite presentation theorem for approximating logic programs. In Proceedings of the 17th ACM Conference on Principles of Programming Languages, pages 197–209, San Francisco, CA, January 1990.
Interval Propagation. In comparison with domain propagation, interval propagation propagates less constraints (that is, leads in general to a weaker constraint store) but is computationally less expensive. For the sake of simplicity let us assume that all finite domain constraints in the store are interval constraints x ∈ [n, m], it is obvious how to generalize to arbitrary finite domains. x ∈ [xl , xu ] ∧ y ∈ [yl , yu ] ∧ z ∈ [zl , zu ] | P lus(x, y, z) → x ∈ [xl , xu ] ∧ y ∈ [yl , yu ] ∧ z ∈ [zl , zu ] | P lus(x, y, z) where for all variables v the values vl are maximal and the values vu are minimal such that • for all variables v we have that vl ≤ vl and vu ≤ vu , and • There are y1 , y2 ∈ [yl , yu ], z1 , z2 ∈ [zl , zu ] such that xl = y1 + z1 and xu = y2 + z2 , and analogously for the other variables.
There are, however, ways to ensure by a careful analysis that a conditional agent still behaves in a logically meaningful way (see, for instance, [AFM96]). The constraint store increases monotonically during the course of a computation, that is if c | ψ →∗ c | ψ then c |= c holds. The consequence is that if in a certain configuration a conditional agent can fire one of its clauses, then he will still be able to fire this clause after further progress of the other agents (provided that the clause has not disappeared due to inconsistency).
Constraints in Computational Logics: Theory and Applications International Summer School, CCL ’99 Gif-sur-Yvette, France, September 5–8, 1999 Revised Lectures by Jean-Pierre Jouannaud, Ralf Treinen (auth.), Gerhard Goos, Juris Hartmanis, Jan van Leeuwen, Hubert Comon, Claude Marché, Ralf Treinen (eds.)