H. Hosobe, S. Matsuoka, and A. Yonezawa. Generalized local propagation: A framework for solving constraint hierarchies. In Proceedings of the Second International Conference on Principles and Practice of Constraint Programming, pages 237--251, 1996.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....constraint hierarchy solvers as well as it provides a base for construction of new solvers in [1] we described several new solvers based on this framework. The basic structure of our framework and terminology is derived from the generalised local propagation by Hosobe, Matsuoka, and Yonezava [11]. Nevertheless, motivation behind our research is different from [11] We intend to generalise local propagation in such a way that it supports arbitrary constraints (in particular, inequalities and comparisons) and comparators (including global comparators) Moreover, in addition to the soundness ....

....of new solvers in [1] we described several new solvers based on this framework. The basic structure of our framework and terminology is derived from the generalised local propagation by Hosobe, Matsuoka, and Yonezava [11] Nevertheless, motivation behind our research is different from [11]. We intend to generalise local propagation in such a way that it supports arbitrary constraints (in particular, inequalities and comparisons) and comparators (including global comparators) Moreover, in addition to the soundness theorem we also prove the completeness theorem. 2 A REFORMULATION ....

Hosobe, H., Matsuoka, S., Yonezawa, A., Generalized Local Propagation: A Framework for Solving Constraint Hierarchies, in: Principles and Practice of Constraint Programming - CP'96 (E. Freuder ed.), pp. 237-251, Springer-Verlag, 1996.

....systems by constraint hierarchies. The problem is probably hidden in hierarchical constraint solvers which utilize different concepts than traditional constraint satisfaction. Additionally, most current hierarchical constraint solvers are constructed for locally better comparators only [8,9,11,12], and thus they are not suitable for inter hierarchy comparison. Also, if the constraint solvers with intrahierarchy comparison are computational hungry, the solvers supporting inter hierarchy comparison are even more demanding for computational power. Despite the current spare usage of ....

Hosobe, H., Matsuoka, S., Yonezawa, A., Generalized Local Propagation: A Framework for Solving Constraint Hierarchies, in: Principles and Practice of Constraint Programming---CP'96 (E. Freuder ed.), Lecture Notes in Computer Science, SpringerVerlag, August 1996

....T W X U Y Z [ a b c e f g h e f g i e j k k g l e m k k o p q s t t w x y z Figure 3 (constraint networks) 3.2. 1 A THEORETICAL JUSTIFICATION A theoretical foundation of our approach originates from ideas described in [11]. In [3] we tuned this theory to the framework proposed in this paper and we proved the soundness and completeness theorems there. A complete formal justification of the proposed framework for solving constraint hierarchies will also be a subject of a separate paper. In the meantime, we satisfy ....

Hosobe, H., Matsuoka, S., Yonezawa, A., Generalized Local Propagation: A Framework for Solving Constraint Hierarchies, in: Principles and Practice of Constraint Programming---CP'96 (E. Freuder ed.), Lecture Notes in Computer Science, Springer-Verlag, August 1996

....systems by constraint hierarchies. The problem is probably hidden in hierarchical constraint solvers which utilize different concepts than traditional constraint satisfaction. Additionaly, most current hierarchical constraint solvers are constructed for locally better comparators only [8,9,11,12], and thus they are not suitable for inter hierarchy comparison. Also, if the constraint solvers with intra hierarchy comparison are computational hungry, the solvers supporting inter hierarchy comparison are even more demanding for computational power. 4 There exists definition of constraint ....

Hosobe, H., Matsuoka, S., Yonezawa, A., Generalized Local Propagation: A Framework for Solving Constraint Hierarchies, in: Principles and Practice of Constraint Programming---CP'96 (E. Freuder ed.), Lecture Notes in Computer Science, Springer-Verlag, August 1996

.... of multi way constraints (e.g. Sannella et al. 1993; Vander Zanden 1996] Indigo [Borning et al. 1996] handles acyclic collections of inequality constraints, but not cycles (simultaneous equations and inequalities) User interface systems that handle simultaneous linear equations include DETAIL [Hosobe et al. 1996] and Ultraviolet [Borning and Freeman Benson 1995] A number of researchers (including the second author) have experimented with a straightforward use of a simplex package in a UI constraint solver, but the speed was not satisfactory for interactive use. Bara# [Bara# 1994] describes a quadratic ....

Hosobe, H., Matsuoka, S., and Yonezawa, A. 1996. Generalized local propagation: A framework for solving constraint hierarchies. In Proceedings of the Second International Conference on Principles and Practice of Constraint Programming (Boston, Aug. 1996).

....as well. Only the core system embracing constraint hierarchies displace the metainterpreter. The same applies to used method for a general hierarchy solver. We present the general hierarchy solver based on refining method there, however, the hierarchy solver using generalized local propagation [10] is almost finished. 4.1 THE KERNEL To illustrate applicability of the plug in architecture of constraint hierarchy solvers we implemented an HCLP interpreter using the proposed architecture. The kernel of the HCLP interpreter consists of: a meta interpreter, much like traditional PROLOG ....

....constraint hierarchy systems as well. Similarly, the presented general hierarchy solver is based on the refining method and the presented flat constraint solver does not support other than predicate comparators. However, we are currently finishing implementation of a generalized local propagation [10] algorithm that is at least partially incremental, thus, more effective. We also expect that this generalized local propagation module enables us to develop an effective constraint hierarchy system with inter hierarchy comparison [20] The complete PROLOG source code of all above mentioned ....

Hosobe, H., Matsuoka, S., Yonezawa, A., Generalized Local Propagation: A Framework for Solving Constraint Hierarchies, in: Principles and Practice of Constraint Programming---CP'96 (E. Freuder ed.), Lecture Notes in Computer Science, Springer-Verlag, August 1996

....and is a kind of meta solver that supports different subsolvers. It supports multi way constraints and constraint hierarchies, using local propagation when possible, and grouping constraint cycles into cells, which are then solved by an appropriate subsolver. The most recent version of DETAIL [19] also includes an experimental solver for inequality constraints as well as functional constraints. Ultraviolet uses the Indigo algorithm [1] for acyclic collections of inequality constraints. Indigo is an interval propagation algorithm; there has been considerable work on interval constraints in ....

Hiroshi Hosobe, Satoshi Matsuoka, and Akinori Yonezawa. Generalized local propagation: A framework for solving constraint hierarchies. In Proceedings of the Second International Conference on Principles and Practice of Constraint Programming, Boston, August 1996.

....[Hosobe et al. 94] is an incremental solver for multi way constraints and constraint hierarchies. It is more general than traditional local propagation, since it allows constraint cycles to be grouped into cells, which are then solved by an appropriate subsolver. The most recent version of DETAIL [Hosobe et al. 96] supports inequality constraints as well as functional constraints, although the current prototype has exponential time complexity. There has been considerable work on interval constraints in other areas of computer science, particularly artificial intelligence and constraint logic programming. ....

Hiroshi Hosobe, Satoshi Matsuoka, and Akinori Yonezawa. Generalized local propagation: A framework for solving constraint hierarchies. In Proceedings of the Second International Conference on Principles and Practice of Constraint Programming, Boston, August 1996.

....algorithms. Examples include QOCA [58] which solves simultaneous linear equation and inequality constraints while optimizing a quadratic expression, Bramble [46] and Juno 2 [65] which use numerical solvers, Indigo [9] an interval propagation algorithm for inequality constraints, and DETAIL [67] and Ultraviolet [10] both of which are hybrid algorithms supporting both local propagation and cycle solvers. 3.4 Constraint Programming in Operations Research Operations Research is a vast field represented by departments in major universities and industrial settings around the world. The ....

H. Hosobe, S. Matsuoka, and A. Yonezawa. Generalized local propagation: A framework for solving constraint hierarchies. In E. C. Freuder, editor, Proceedings of the International Conference on Principles and Practice of Constraint Programming, volume 1118, Boston, 1996. Springer-Verlag.

.... 16] or local propagation algorithms for acyclic collections of multi way constraints (e.g. 18, 20] Indigo [2] handles acyclic collections of inequality constraints, but not cycles (simultaneous equations and inequalities) UI systems that handle simultaneous linear equations include DETAIL [11] and Ultraviolet [3] A number of researchers (including the first author) have experimented with a straightforward use of a simplex package in a UI constraint solver, but the speed was not satisfactory for interactive use. An earlier version of QOCA is described in references [9] and [10] These ....

Hiroshi Hosobe, Satoshi Matsuoka, and Akinori Yonezawa. Generalized local propagation: A framework for solving constraint hierarchies. In Proceedings of the Second International Conference on Principles and Practice of Constraint Programming, Boston, August 1996.

....[11] DETAIL [13] is an incremental solver for multi way constraints and constraint hierarchies. It is more general than traditional local propagation, since it allows constraint cycles to be grouped into cells, which are then solved by an appropriate subsolver. The most recent version of DETAIL [12] supports inequality constraints as well as functional constraints, although the current prototype has exponential time complexity. There has been considerable work on interval constraints in other areas of computer science, particularly artificial intelligence and constraint logic programming. ....

Hiroshi Hosobe, Satoshi Matsuoka, and Akinori Yonezawa. Generalized local propagation: A framework for solving constraint hierarchies. In Proceedings of the Second International Conference on Principles and Practice of Constraint Programming, Boston, August 1996.

.... 15] or local propagation algorithms for acyclic collections of multiway constraints (e.g. 17, 19] Indigo [2] handles acyclic collections of inequality constraints, but not cycles (simultaneous equations and inequalities) UI systems that handle simultaneous linear equations include DETAIL [10] and Ultraviolet [3] A number of researchers (including the second author) have experimented with a straightforward use of a simplex package in a UI constraint solver, but the speed was not satisfactory for interactive use. Bara# [1] describes a quadratic optimization algorithm for solving linear ....

....ways to form a totally ordered set of constraints, and taking the union of the sets of solutions to each of these totally ordered hierarchies. For example, consider the two constraints weak x = 0 and weak x = 10. The set of LEB solutions is the infinite set of mappings from x to each number in [0, 10]. Assuming equal weights on the constraints, the (single) least squares solution is x ## 5 . The TLEB solutions are defined by producing all the totally ordered hierarchies and taking the union of their solutions. In this case the two possible total orderings are: weak x = 0, slightly weaker x ....

Hiroshi Hosobe, Satoshi Matsuoka, and Akinori Yonezawa. Generalized local propagation: A framework for solving constraint hierarchies. In Proceedings of the Second International Conference on Principles and Practice of Constraint Programming, Boston, August 1996.

.... 17] or local propagation algorithms for acyclic collections of multi way constraints (e.g. 19, 21] Indigo [2] handles acyclic collections of inequality constraints, but not cycles (simultaneous equations and inequalities) UI systems that handle simultaneous linear equations include DETAIL [12] and Ultraviolet [3] A number of researchers (including the first author) have experimented with a straightforward use of a simplex package in a UI constraint solver, but the speed was not satisfactory for interactive use. An earlier version of QOCA is described in references [10] and [11] These ....

H. Hosobe, S. Matsuoka, and A. Yonezawa. Generalized local propagation: A framework for solving constraint hierarchies. In Proc. Constraint Programming'96, Springer-Verlag LNCS Vol 1118, Aug 1996.

....use one way constraints (e.g. 9, 10] or local propagation algorithms for acyclic collections of multi way constraints (e.g. 12, 15] Indigo [2] handles acyclic collections of inequality constraints, but not cycles. UI systems that handle simultaneous (cyclic) linear equations include DETAIL [8] and Ultraviolet [3] UI systems that handle simultaneous linear inequalities as well with reasonable efficiency are QOCA [7] and Cassowary [6] Both of these algorithms are based on the simplex algorithm. We provide timing comparisons between Cassowary and our Fourier compilation algorithm in ....

H. Hosobe, S. Matsuoka, and A. Yonezawa. Generalized local propagation: A framework for solving constraint hierarchies. In Procs. of the CP96, Boston, 1996.

....propagation guarantees that it obtains correct solutions of HCSs. As a first step, we focus on the theory of constraint hierarchies, which is, as noted, one of the most popular formulations of HCSs. To begin with, we CHAPTER 1. INTRODUCTION 8 reformulate the theory with a more strict definition [38]. Next, we propose generalized local propagation (GLP) as a framework for studying constraint hierarchies, and show properties useful for constraint satisfaction [36, 38] Then applying the result of GLP, we develop the DETAIL constraint solver, whose algorithm is the first local propagation ....

....popular formulations of HCSs. To begin with, we CHAPTER 1. INTRODUCTION 8 reformulate the theory with a more strict definition [38] Next, we propose generalized local propagation (GLP) as a framework for studying constraint hierarchies, and show properties useful for constraint satisfaction [36, 38]. Then applying the result of GLP, we develop the DETAIL constraint solver, whose algorithm is the first local propagation method that solves constraint hierarchies with a global criterion [39, 40] Past local propagation algorithms treat dataflow constraints, although most practical applications ....

Hosobe, H., S. Matsuoka, and A. Yonezawa, "Generalized Local Propagation: A Framework for Solving Constraint Hierarchies," in Principles and Practice of Constraint Programming---CP'96 (E. C. Freuder, ed.), vol. 1118 of Lecture Notes in Computer Science, Springer-Verlag, Aug. 1996, pp. 237--251.

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H. Hosobe, S. Matsuoka, and A. Yonezawa. Generalized local propagation: A framework for solving constraint hierarchies. In Proceedings of the Second International Conference on Principles and Practice of Constraint Programming, pages 237--251, 1996.

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H. Hosobe, S. Matsuoka, and A. Yonezawa. Generalized local propagation: A framework for solving constraint hierarchies. In Proceedings of the Second International Conference on Principles and Practice of Constraint Programming, pages 237--251, 1996.

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Hosobe, H., Matsuoka, S., Yonezawa, A., Generalized Local Propagation: A Framework for Solving Constraint Hierarchies, in: Principles and Practice of Constraint Programming---CP'96 (E. Freuder ed.), Lecture Notes in Computer Science, Springer-Verlag, August 1996

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Hosobe, H., Matsuoka, S., Yonezawa, A., Generalized Local Propagation: A Framework for Solving Constraint Hierarchies, in: Principles and Practice of Constraint Programming---CP'96 (E. Freuder ed.), Lecture Notes in Computer Science, Springer-Verlag, August 1996

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