Philosophy of the MiniZinc challenge

Stuckey, Peter J.; Becket, Ralph; Fischer, Julien

doi:10.1007/s10601-010-9093-0

Cited by 64 publications

(58 citation statements)

References 3 publications

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“…They can be translated in a non-parameterized (instantiated) low-level format called FlatZinc that can contain solver-dependent constructs. This format is understood by a wide range of different types of solvers [16], such as CP solvers, MIP (Mixed Integer Linear Programming) solvers, SAT (Boolean Satisfiability) and SMT (SAT-Modulo-Theories) solvers.…”

Section: Minizinc and Itemset Mining In Minizincmentioning

confidence: 99%

MiningZinc: A declarative framework for constraint-based mining

Guns¹,

Dries²,

Nijssen³

et al. 2017

Artificial Intelligence

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We introduce MiningZinc, a declarative framework for constraint-based data mining. MiningZinc consists of two key components: a language component and an execution mechanism.First, the MiningZinc language allows for high-level and natural modeling of mining problems, so that MiningZinc models are similar to the mathematical definitions used in the literature. It is inspired by the Zinc family of languages and systems and supports user-defined constraints and functions.Secondly, the MiningZinc execution mechanism specifies how to compute solutions for the models. It is solver independent and supports both standard constraint solvers and specialized data mining systems. The high-level problem specification is first translated into a normalized constraint language (FlatZinc). Rewrite rules are then used to add redundant constraints or solve subproblems using specialized data mining algorithms or generic constraint programming solvers. Given a model, different execution strategies are automatically extracted that correspond to different sequences of algorithms to run. Optimized data mining algorithms, specialized processing routines and generic solvers can all be automatically combined.Thus, the MiningZinc language allows one to model constraint-based itemset mining problems in a solver independent way, and its execution mechanism can automatically chain different algorithms and solvers. This leads to a unique combination of declarative modeling with high-performance solving.

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Section: Minizinc and Itemset Mining In Minizincmentioning

confidence: 99%

MiningZinc: A declarative framework for constraint-based mining

Guns¹,

Dries²,

Nijssen³

et al. 2017

Artificial Intelligence

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“…In each case, we can check that each empty square is dominated and that none of the dragon horses attack each other. We set up models in MiniZinc [7,9] for the domination and independent domination numbers of H n and applied the G12 MIP solver to those models. The model for i(H n ) is shown in Figure 9.…”

Section: Dragon Horsesmentioning

confidence: 99%

Independence and domination on shogiboard graphs

Chatham

2017

Recreational Mathematics Magazine

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Given a (symmetrically-moving) piece from a chesslike game, such as shogi, and an n × n board, we can form a graph with a vertex for each square and an edge between two vertices if the piece can move from one vertex to the other. We consider two pieces from shogi: the dragon king, which moves like a rook and king from chess, and the dragon horse, which moves like a bishop and rook from chess. We show that the independence number for the dragon kings graph equals the independence number for the queens graph. We show that the (independent) domination number of the dragon kings graph is n − 2 for 4 n 6 and n − 3 for n 7. For the dragon horses graph, we show that the independence number is 2n − 3 for n 5, the domination number is at most n − 1 for n 4, and the independent domination number is at most n for n 5.

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“…Modelling languages such as MiniZinc aim to decouple modelling and search, so that the constraint model can be compiled for a variety of diferent solvers, including SAT [21,28].…”

Section: Related Workmentioning

confidence: 99%

Theory learning with symmetry breaking

Howe

Robbins

King

2017

Proceedings of the 19th International Symposium on Principles and Practice of Declarative Programming

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This paper investigates the use of a Prolog coded SMT solver in tackling a well known constraints problem, namely packing a given set of consecutive squares into a given rectangle, and details the developments in the solver that this motivates. The packing problem has a natural model in the theory of quantiier-free integer diference logic, a theory supported by many SMT solvers. The solver used in this work exploits a data structure consisting of an incremental Floyd-Warshall matrix paired with a watch matrix that monitors the entailment status of integer diference constraints. It is shown how this structure can be used to build unsatisiable theory cores on the ly, which in turn allows theory learning to be incorporated into the solver. Further, it is shown that a problem-speciic and non-standard approach to learning can be taken where symmetry breaking is incorporated into the learning stage, magnifying the efect of learning. It is argued that the declarative framework allows the solver to be used in this white box manner and is a strength of the solver. The approach is experimentally evaluated. CCS CONCEPTS· Theory of computation → Shortest paths; Packing and covering problems; · Software and its engineering → Constraint and logic languages;

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Philosophy of the MiniZinc challenge

Cited by 64 publications

References 3 publications

MiningZinc: A declarative framework for constraint-based mining

MiningZinc: A declarative framework for constraint-based mining

Independence and domination on shogiboard graphs

Theory learning with symmetry breaking

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