Borning, A., Maher, M., Martindale, A., Wilson, M.: Constraint hierarchies and logic programming. In Levi, G., Martelli, M., eds.: ICLP'89: Proceedings 6th International Conference on Logic Programming, Lisbon, Portugal, MIT Press (1989) 149-164  Home/Search   Document Details and Download   Summary   Related Articles   Check

.... if we swap the strong and the weak constraints, we may get a completely different solution (whitedenim cordovans, white blue cordovans) The idea of putting preferences to individual constraints is formalised in constraint hierarchies that have been proposed first in [4] and further developed in [5]. In constraint hierarchies, each constraint is labelled by a preference expressing the strength of the constraint we are speaking about the labelled constraint. Usually blue denim grey 4 names like required, strong, medium, weak, and weakest are used to denote the preferences. The preferences ....

Borning, A., Maher, M., Martindale, A., Wilson, M., Constraint Hierarchies and Logic Programming, in Proceedings of the Sixth International Conference on Logic Programming, pp. 149-164, Lisbon, 1989.

....framework: k relevance allows the simulating of constraint retraction addition and so provides richer diagnosis tools. Our current work includes designing algorithms which compute the best conflict set (see Section 3.3) from all the explanations. For this, we plan to add user based comparators [25] in order to provide automatic comparison of solutions. Also, we are trying to decrease the space required to manage the explanations. ....

Borning, A., Maher, M., Martindale, A., Wilson, M.: Constraint hierarchies and logic programming. In Levi, G., Martelli, M., eds.: ICLP'89: Proceedings 6th International Conference on Logic Programming, Lisbon, Portugal, MIT Press (1989) 149--164

....within a classical framework: k relevance allows the simulating of constraint retrac tion addition and so provides richer diagnosis tools. Our current work includes designing algorithms which compute the best conflict from all the explanations. We investigate adding user based comparators [23] to our tools in order to provide automatic comparison of solutions. Also, we try to decrease the space complexity by managing differently the explanations. ....

Borning, A., Maher, M., Martindale, A., Wilson, M.: Constraint hierarchies and logic programming. In Levi, G., Martelli, M., eds.: ICLP'89: Proceedings 6th International Conference on Logic Programming, Lisbon, Portugal, MIT Press (1989) 149-164

....is called a comparator and its main feature is respecting the hierarchy, i.e. the comparator prefers the valuations satisfying the stronger constraints. It is possible to define various comparators like locally better or globally better comparators, for details and precise definitions see [4,6,16]. An important aspect of constraint hierarchies is the existence of efficient satisfaction algorithms constraint hierarchy solvers. In this paper, we are interested in two groups of these solvers: algorithms based on refining method and local propagation algorithms. The refining algorithms ....

....The refining method was first used in a simple interpreter for HCLP (Hierarchical 1 Charles University, Faculty of Mathematics and Physics, Malostransk nmest 2 25, Praha, Czech Republic. bartak kti.mff.cuni.cz. Supported by GACR grant 201 99 D057. Constraint Logic Programming) programs [6] and it is also employed in the DeltaStar algorithm [16] and in the HCLP language CHAL [14] The refining method is general it can be applied to any constraint hierarchy using any comparator. However, this method requires the solution to be recomputed from scratch after every change (e.g. after ....

[Article contains additional citation context not shown here]

Borning, A., Maher, M., Martindale, A., Wilson, M., Constraint Hierarchies and Logic Programming, in: Proceedings of the Sixth International Conference on Logic Programming, pp. 149-164, Lisbon, 1989.

....classical constraints can be seen as a specific class of soft constraints, where there are only two levels of preference false and true with true better than false) and logical and is the combinator operator. Many formalisms have been developed to describe one or more classes of soft constraints [6, 7, 3]. In this paper we refer to one which is general enough to describe most of the desired classes. This framework is based on a semiring structure, that is, a set plus two operators: the set contains all the preference levels, one of the operators gives the order over such a set, while the other one ....

....which is also flexible and adaptable. And finally, we develop a general implementation which can be used for many di#erent classes of soft constraints, and also to combine some of them. 2 Soft Constraints In the literature there have been many formalizations of the concept of soft constraints [6, 7, 3]. Here we refer to a specific one [2] which however can be shown to generalize and express many of the others. In short, a soft constraint is a constraint where each instantiation of its variables has an associated value from a partially ordered set. Combining constraints will then have to take ....

A. Borning, M. Maher, A. Martindale, and M. Wilson. Constraint hierarchies and logic programming. In Martelli M. Levi G., editor, Proc. 6th International Conference on Logic Programming, pages 149--164. MIT Press, 1989.

....bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and or a fee. SAC 2002 Madrid, Spain Copyright 2002 ACM 1 58113 445 2 02 03 . 5.00. more classes of soft constraints [6, 7, 3]. In this paper we refer to one which is general enough to describe most of the desired classes. This framework is based on a semiring structure, that is, a set plus two operators: the set contains all the preference levels, one of the operators gives the order over such a set, while the other one ....

A. Borning, M. Maher, A. Martindale, and M. Wilson. Constraint hierarchies and logic programming. In Proc. 6th International Conference on Logic Programming, pages 149-164. MIT Press, 1989.

....bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and or a fee. SAC 2002 Madrid, Spain Copyright 2002 ACM 1 58113 445 2 02 03 . 5.00. more classes of soft constraints [6, 7, 3]. In this paper we refer to one which is general enough to describe most of the desired classes. This framework is based on a semiring structure, that is, a set plus two operators: the set contains all the preference levels, one of the operators gives the order over such a set, while the other one ....

A. Borning, M. Maher, A. Martindale, and M. Wilson. Constraint hierarchies and logic programming. In Proc. 6th International Conference on Logic Programming, pages 149--164. MIT Press, 1989.

....classical constraints can be seen as a speci c class of soft constraints, where there are only two levels of preference false and true with true better than false) and logical and is the combinator operator. Many formalisms have been developed to describe one or more classes of soft constraints [6, 7, 3]. In this paper we refer to one which is general enough to describe most of the desired classes. This framework is based on a semiring structure, that is, a set plus two operators: the set contains all the preference levels, one of the operators gives the order over such a set, while the other one ....

....which is also exible and adaptable. And nally, we develop a general implementation which can be used for many di erent classes of soft constraints, and also to combine some of them. 2 Soft Constraints In the literature there have been many formalizations of the concept of soft constraints [6, 7, 3]. Here we refer to a speci c one [2] which however can be shown to generalize and express many of the others. In short, a soft constraint is a constraint where each instantiation of its variables has an associated value from a partially ordered set. Combining constraints will then have to take ....

[Article contains additional citation context not shown here]

A. Borning, M. Maher, A. Martindale, and M. Wilson. Constraint hierarchies and logic programming. In Martelli M. Levi G., editor, Proc. 6th International Conference on Logic Programming, pages 149-164. MIT Press, 1989.

....to values which has the best value for the chosen criterion. Additional complexity is present when we have different criteria (like to maximize the levels of importance, and also to minimize the sum of all costs) Research in this area first started with the hierarchical CLP (HCLP) system [25], a CLP language where each constraint has a level of importance and a solution of a constraint problem is found by respecting the hierarchy of constraints. Then much work on fuzzy constraints, where each constraint has associated a value between 0 and 1 and we have to maximize the minimum of such ....

....To solve this second problem, one approach is to ask the end user to make some decisions, as in the interactive problem solving applications described above. Otherwise, we can associate a cost to each constraint, or form a hierarchy of constraints (where higher constraints are more important) [25], or, more generally, resort to soft constraints [66, 58, 150, 155, 20] Typical over constrained problems are timetabling problems, where constraints come from many sources (like the availability of rooms and all the professors preferences) A recent system uses both hard and soft constraints ....

A. Borning, M. Maher, A. Martindale, and M. Wilson. Constraint hierarchies and logic programming. In Martelli M. Levi G., editor, Proc. 6th International Conference on Logic Programming, pages 149--164. MIT Press, 1989.

....to republish, to post on servers, or to redistribute to lists requires prior speci c permission and or a fee. c ACM Transactions on Programming Languages and Systems, Vol. No. Pages 1 29. 2 Stefano Bistarelli et al. partiality [Freuder and Wallace 1992] hierarchy [Borning et al. 1989], and optimization. This framework is based on the observation that a semiring (that is, a domain plus two operations satisfying certain properties) is all what is needed to describe many constraint satisfaction schemes. In fact, the domain of the semiring provides the levels of consistency (which ....

....still a semiring (see [Bistarelli et al. 1995; 1997a] and thus optimization based on multiple criteria can be cast in our framework as well. From another point of view, where classical constraints are extended to have several degrees of satisfaction, a related approach is HCLP (Hierarchical CLP) [Borning et al. 1989], where each constraint has a level of importance (like strong, weak, required) and these levels are used to decide which constraints to satisfy. However, a constraint can only be satis ed or not, and thus HCLP is a crisp formalism. Moreover, their treatment is only algorithmic, and they do not ....

[Article contains additional citation context not shown here]

Borning, A., Maher, M., Martindale, A., and Wilson, M. 1989. Constraint hierarchies and logic programming. In Proc. 6th International Conference on Logic Programming, G. Levi and M. Martelli, Eds. MIT Press, Cambridge, MA, USA, 149-164.

....at a given level using some combining function. Thus, the globally better comparators can be used for inter hierarchy comparison [22] i.e. comparison of solutions to two or more constraint hierarchies. The theory of constraint hierarchies was developed in [6] and it is also described in [8,22,23]. The existence of efficient satisfaction algorithms is another important aspect of constraint hierarchies. Most current satisfaction algorithms, in other words constraint hierarchy solvers, can be classified into two groups: algorithms based on refining method and local propagation algorithms. ....

....in a straightforward manner by completely satisfying the strongest level first and then weaker levels successively. Thus, the refining method can be used for solving all constraint hierarchies using arbitrary comparator. The refining method was first used in a simple interpreter for HCLP programs [8] and it is also employed in the DeltaStar algorithm [23] and in a hierarchical constraint logic programming language CHAL. We show later (Section 3.3) that our generalized framework for solving constraint hierarchies covers the refining method as well. The local propagation algorithms take an ....

Borning, A., Maher, M., Martindale, A., Wilson, M., Constraint Hierarchies and Logic Programming, in: Proceedings of the Sixth International Conference on Logic Programming, pp. 149-164, Lisbon, June, 1989 &

....programming (HCLP) language. 1 Introduction This paper presents a method of disambiguation task by a variant of circumscription, prioritized circumscription (McCarthy, 1986; Lifschitz, 1985) and discuss its implementation by a hierarchical constraint logic programming (HCLP) language such as (Borning et al. 1989). Disambiguation is a very important task in natural language processing. To resolve ambiguity, humans seem to use not only syntactic constraints but also various levels of heuristics such as grammatical preferences (Hobbs, 1990) and semantic preferences (Wilks, 1975) For example, suppose that ....

....single preferable model. So, if there are yet multiple possible readings as a result of disambiguation, we can keep these possible readings as multiple preferable models. In this paper, we also discuss an implementation by using hierarchical constraint logic programming (HCLP) language such as (Borning et al. 1989). HCLP language is similar to constraint logic programming language except that we can represent a constraint hierarchy. Thus, there is a correspondence between a solution of an HCLP language and the most preferable models of prioritized circumscription. In this paper, we use our HCLP language ....

[Article contains additional citation context not shown here]

Borning, A., Maher, M., Martindale, A., and Wilson, M. 1989. Constraint Hierarchies and Logic Programming. In Proceedings of the International Conference on Logic Programming, pages 149 -- 164.

....support for identifying inconsistent subsets of constraints on failure. The query interface of CLP( languages can be enhanced by exploiting the results of failure analysis. ffl Constraint failure analysis can also be very beneficial in system with preferences on constraints like the one of [BMMW89]. The algorithm presented in [BMMW89] on failure blindly subtracts constraints with less preference from the set of constraints until a satisfiable set is reached. Knowing which constraints are in conflict can be very useful in such a system. To summarise, the results of constraint failure ....

....subsets of constraints on failure. The query interface of CLP( languages can be enhanced by exploiting the results of failure analysis. ffl Constraint failure analysis can also be very beneficial in system with preferences on constraints like the one of [BMMW89] The algorithm presented in [BMMW89] on failure blindly subtracts constraints with less preference from the set of constraints until a satisfiable set is reached. Knowing which constraints are in conflict can be very useful in such a system. To summarise, the results of constraint failure analysis presented in this chapter can have ....

[Article contains additional citation context not shown here]

A. Borning, M. Maher, A. Martindale, and M. Wilson. Constraint hierarchies and logic programming. In G. Levi and M. Martelli, editors, Proceedings of the Sixth International Conference on Logic Programming, pages 149--164, Lisbon, 1989. The MIT Press.

....as is the normal behaviour of a CLP deduction. For an over constrained system to produce a solution, some of the conflicting constraints have to be (automatically) relaxed. This can, for example, be achieved by extending the CLP paradigm with hierarchical constraints such as in the HCLP framework (Borning et al. 1989; Wilson Borning 1993) As yet we have not investigated methods to support over constrained specifications in our framework. A good overview of recent research into over constrained systems can be found in (Jampel, Freuder, Maher 1996) Under constrained systems, on the other hand, are of no ....

Borning, A.; Maher, M.; Martindale, A.; and Wilson, M. 1989. Constraint hierarchies and logic programming. In Levi, G., and Martelli, M., eds., International Conference on Logic Programming. MIT Press. 149 -- 164.

....solvers must address and some of the approaches for remaining efficient are similar. Constraint systems for interactive graphics must deal with under constrained systems. An under constrained system has remaining degrees of freedom so multiple possible solutions exist. Constraint hierarchies [14, 17, 48] provide a popular and well studied means of removing ambiguity by over constraining the system with constraints at decreasing levels of preference (see Section 2.2) A related concern for solvers is to maintain spatial stability of the system. When a geometric system is under constrained, ....

....aspects of the solver s behavior. For example, the user textually orders the listing of the satisfaction methods to indicate which assignment should be performed when multiple possibilities exist. This type of meta constraint was later replaced by the now classic notion of a constraint hierarchy [17, 48] where constraints may be specified at multiple levels of preference. 7 7 The DeltaStar solver shown in Figure 2.1 was designed simply to aid research in constraint hierarchies by parame 24 x1 x2 r m x1 x2 r m C1 C4 C3 C1 C4 C3 Figure 2.6: Two possible plans for executing Figure ....

Alan Borning, Michael Maher, Amy Martindale, and Molly Wilson. Constraint hierarchies and logic programming. In Proceedings of the Sixth International Conference on Logic Programming, pages 149--164, Lisbon, June 1989.

....preference (taken from the semiring) The two semiring operations define how to combine constraints together and how to compare different solutions. Many existing extensions to the classical constraint framework, such as fuzzy [4, 13] partial [6] probabilistic [5] and hierarchical constraints [3], can all be cast within the SCSP framework. Although being very expressive, soft constraint problems can make the modeling phase much more difficult, since one has to specify all preferences for every possible combination of values in each constraint. Moreover, sometimes one may know his her ....

A. Borning, M. Maher, A. Martindale, and M. Wilson. Constraint hierarchies and logic programming. In Martelli M. Levi G., editor, Proc. 6th International Conference on Logic Programming, pages 149--164. MIT Press, 1989.

No context found.

Alan Borning, Michael Maher, Amy Martindale and Molly Wilson, `Constraint hierarchies and logic programming', Proc. Sixth International Conference on Logic Programming, Lisbon, June 1989, p. 149--164.

....is parameterized by a comparator C that allows us to compare different possible solutions to a single hierarchy and select the best ones. Recently this constraint hierarchy paradigm was integrated with the Constraint Logic Programming scheme [6] to produce Hierarchical Constraint Logic Programming [2]. Like CLP, HCLP is parameterized by D, the domain of the constraints. In addition, HCLP is parameterized by the comparator C. This integration of CLP and constraint hierarchies allows the full programming capabilities and theoretical foundation of logic programming to complement the ....

....by D, the domain of the constraints. In addition, HCLP is parameterized by the comparator C. This integration of CLP and constraint hierarchies allows the full programming capabilities and theoretical foundation of logic programming to complement the expressiveness of preferential constraints. In [2] we described a prototype implementation of HCLP(R;LPB) where R is the domain of real numbers and LPB is the locally predicate better comparator (defined in the next section) Experience with writing programs in HCLP(R;LPB) has provided us with many examples where the LPB comparator fails to rule ....

[Article contains additional citation context not shown here]

Alan Borning, Michael Maher, Amy Martindale, and Molly Wilson. Constraint Hierarchies and Logic Programming. In Proceedings of the Sixth International Conference on Logic Programming, pages 149--164, Lisbon, June 1989.

No context found.

Borning, A., Maher, M., Martindale, A., Wilson, M.: Constraint hierarchies and logic programming. In Levi, G., Martelli, M., eds.: ICLP'89: Proceedings 6th International Conference on Logic Programming, Lisbon, Portugal, MIT Press (1989) 149-164

No context found.

A. Borning, M. Maher, A. Martindale, and M. Wilson (1989). Constraint hierarchies and logic programming. In M. Martell, Levi G., editors, Proc. 6th ICLP. MIT Press.

No context found.

A. Borning, M. Maher, A. Martindale, and M. Wilson. Constraint hierarchies and logic programming. In Martelli M. Levi G., editor, Proc. 6th International Conference on Logic Programming, pages 149--164. MIT Press, 1989.

No context found.

A. Borning, M. Maher, A. Martindale, and M. Wilson. Constraint hierarchies and logic programming. Proceedings of the Sixth International Conference on Logic Programming, pages 149-164, 1989.

No context found.

Borning, A., Mahert, M., Martindale, A., and Wilson, M. Constraint hierarchies and logic programming. In Int. conf. on logic programming (1989), pp. 149--164.

No context found.

Borning, A., Maher, M., Martindale, A., Wilson, M., Constraint Hierarchies and Logic Programming, in: Proceedings of the Sixth International Conference on Logic Programming, pp. 149-164, Lisbon, June, 1989

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Borning, A., Maher, M., Martindale, A., Wilson, M., Constraint Hierarchies and Logic Programming, in: Proceedings of the Sixth International Conference on Logic Programming, pp. 149-164, Lisbon, June, 1989

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