Intelligent search strategies based on adaptive Constraint Handling Rules

Wolf, Armin

doi:10.1017/s1471068405002383

Cited by 13 publications

(14 citation statements)

References 16 publications

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“…Moreover we prove confluence of the rule scheme for logical retraction with the rules of the given program. The application of adaptive CHR considered dynamic constraint satisfaction problems (DCSP) only, in particular for the implementation of search strategies [Wol05], while we apply our approach to arbitrary algorithms in order to make them fully dynamic.…”

Section: Related Workmentioning

confidence: 99%

Justifications in Constraint Handling Rules for Logical Retraction in Dynamic Algorithms

Frühwirth¹

2018

Logic-Based Program Synthesis and Transformation

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We present a straightforward source-to-source transformation that introduces justifications for user-defined constraints into the CHR programming language. Then a scheme of two rules suffices to allow for logical retraction (deletion, removal) of constraints during computation. Without the need to recompute from scratch, these rules remove not only the constraint but also undo all consequences of the rule applications that involved the constraint. We prove a confluence result concerning the rule scheme and show its correctness. When algorithms are written in CHR, constraints represent both data and operations. CHR is already incremental by nature, i.e. constraints can be added at runtime. Logical retraction adds decrementality. Hence any algorithm written in CHR with justifications will become fully dynamic. Operations can be undone and data can be removed at any point in the computation without compromising the correctness of the result. We present two classical examples of dynamic algorithms, written in our prototype implementation of CHR with justifications that is available online: maintaining the minimum of a changing set of numbers and shortest paths in a graph whose edges change.

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Section: Related Workmentioning

confidence: 99%

Justifications in Constraint Handling Rules for Logical Retraction in Dynamic Algorithms

Frühwirth¹

2018

Logic-Based Program Synthesis and Transformation

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“…An adaptive semantics is defined in [136]. Adaptive CHR is used for realizing intelligent search strategies in [135,136].…”

Section: Chr Language Variants and Extensionsmentioning

confidence: 99%

Constraint Handling Rules - What Else?

Frühwirth

2015

Rule Technologies: Foundations, Tools, and Applications

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Abstract. Constraint Handling Rules (CHR) is both an effective concurrent declarative constraint-based programming language and a versatile computational formalism. While conceptually simple, CHR is distinguished by a remarkable combination of desirable features:-a semantic foundation in classical and linear logic, -an effective and efficient sequential and parallel execution model -guaranteed properties like the anytime online algorithm properties -powerful analysis methods for deciding essential program properties. This overview of some CHR-related research and applications is by no means meant to be complete. Essential introductory reading for CHR provide the survey article [125] and the books [56,63]. Up-to-date information on CHR can be found online at the CHR web-page www. constraint-handling-rules.org, including the slides of the keynote talk associated with this article. In addition, the CHR website dtai. cs.kuleuven.be/CHR/ offers everything you want to know about CHR, including online demo versions and free downloads of the language. Executive SummaryConstraint Handling Rules (CHR) [56] tries to bridge the gap between theory and practice, between logical specification and executable program by abstraction through constraints and the concepts of computational logic. CHR has its roots in constraint logic programming and concurrent constraint programming, but also integrates ideas from multiset transformation and rewriting systems as well as automated reasoning and theorem proving. It seamlessly blends multi-headed rewriting and concurrent constraint logic programming into a compact userfriendly rule-based programming language. CHR consists of guarded reactive rules that transform multisets of relations called constraints until no more change occurs. By the notion of constraint, CHR does not need to distinguish between data and operations, and its rules are both descriptive and executable.In CHR, one distinguishes two main kinds of rules: Simplification rules replace constraints by simpler constraints while preserving logical equivalence, e.g., X≤Y∧Y≤X ⇔ X=Y. Propagation rules add new constraints that are logically redundant but may cause further simplification, e.g., X≤Y∧Y≤Z ⇒ X≤Z. Together with X≤X ⇔ true, these rules encode the axioms of a partial order relation. The rules compute its transitive closure and replace inequalities ≤ by equalities = whenever possible. For example, A≤B∧B≤C∧C≤A becomes A=B∧B=C. More program examples can be found in Section 2. Semantics of CHR are discussed in Section 3.

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“…CHR V Constraint Handling Rules with Disjunction (CHRv) [Abdennadher and Schutz, 1998] is a general concurrent logic programming language, rule-based, which have been adapted to a wide set of applications such as: constraint satisfaction [Wolf, 2005], abduction [Gavanelli et al, 2008], component development engineering [Fages et al, 2008], and so on. It is designed for creation of constraint solvers.…”

Section: B Search Heuristicsmentioning

confidence: 99%

LSVF: a New Search Heuristic to Reduce the Backtracking Calls for Solving Constraint Satisfaction Problem

Rodrigues¹,

Azevedo²,

Freitas³

et al. 2012

IJARAI

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Abstract-Many researchers in Artificial Intelligence seek for new algorithms to reduce the amount of memory/ time consumed for general searches in Constraint Satisfaction Problems. These improvements are accomplished by the use of heuristics which either prune useless tree search branches or even indicate the path to reach the (optimal) solution faster than the blind version of the search. Many heuristics were proposed in the literature, like the Least Constraining Value (LCV). In this paper we propose a new pre-processing search heuristic to reduce the amount of backtracking calls, namely the Least Suggested Value First: a solution whenever the LCV solely cannot measure how much a value is constrained. In this paper, we present a pedagogical example, as well as the preliminary results.

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Intelligent search strategies based on adaptive Constraint Handling Rules

Cited by 13 publications

References 16 publications

Justifications in Constraint Handling Rules for Logical Retraction in Dynamic Algorithms

Justifications in Constraint Handling Rules for Logical Retraction in Dynamic Algorithms

Constraint Handling Rules - What Else?

LSVF: a New Search Heuristic to Reduce the Backtracking Calls for Solving Constraint Satisfaction Problem

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