the proceedings contain 38 papers. the topics discussed include: solving patience and solitaire games with good old fashioned AI;the complexity of symmetry breaking beyond Lex-Leader;certifying without loss of general...
ISBN:
(纸本)9783959773362
the proceedings contain 38 papers. the topics discussed include: solving patience and solitaire games with good old fashioned AI;the complexity of symmetry breaking beyond Lex-Leader;certifying without loss of generality reasoning in solution-improving maximum satisfiability;ParLS-PBO: a parallel local search solver for pseudo Boolean optimization;deep cooperation of local search and unit propagation techniques;cumulative scheduling with calendars and overtime;pseudo-Boolean reasoning about states and transitions to certify dynamic programming and decision diagram algorithms;anytime weighted model counting with approximation guarantees for probabilistic inference;and a multi-stage proof logging framework to certify the correctness of cp solvers.
the proceedings contain 14 papers. the topics discussed include: configuration of heterogeneous agent fleet: a preliminary generic model;challenges in automotive hardware-software co-configuration;prospective and retr...
the proceedings contain 14 papers. the topics discussed include: configuration of heterogeneous agent fleet: a preliminary generic model;challenges in automotive hardware-software co-configuration;prospective and retrospective approaches to integrate life cycle assessment in configurators: a multiple case study in the construction industry;premises, challenges and suggestions for modelling building knowledge using the configuration paradigm;requirements and architectures for green configuration;developing an algorithm selector for green configuration in scheduling problems;instance configuration for sustainable job shop scheduling;product visualization in configurators: laying the foundations for a comparative description;and using answer set programming for assigning tasks to computing nodes.
principles and practice of constraintprogramming--cp98 : 4thinternationalconference, cp98, Pisa, Italy, October 26-30, 1998 : Proceedings by cp98 (1998 : Pisa, Italy); Maher, Michael, 1959-; Puget, Jean-Francois; p...
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principles and practice of constraintprogramming--cp98 : 4thinternationalconference, cp98, Pisa, Italy, October 26-30, 1998 : Proceedings by cp98 (1998 : Pisa, Italy); Maher, Michael, 1959-; Puget, Jean-Francois; published by Berlin ; New York : Springer
constraintprogramming (cp) and Machine Learning (ML) face challenges in text generation due to cp's struggle with implementing "meaning"and ML's difficulty with structural constraints. this paper pr...
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We present a novel scheduling model that leverages constraintprogramming (cp) to enhance problem solving performance in Temporal Planning. Building on the established strategy of decomposing causal and temporal reaso...
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In project scheduling, calendar considerations can increase the duration of a task when its execution overlaps with holidays. On the other hand, the use of overtime may decrease the task's duration. We introduce t...
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We investigate using constraintprogramming (cp) and Domain-Independent Dynamic programming (DIDP) to solve the master problem in Logic-based Benders Decomposition (LBBD) models, in particular addressing the challenge...
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the success of constraintprogramming relies partly on the global constraints and implementation of the associated filtering algorithms. Recently, new ideas emerged to improve these implementations in practice, especi...
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A conceptual clustering is a set of formal concepts (i.e., closed itemsets) that defines a partition of a set of transactions. Finding a conceptual clustering is an NP-complete problem for which constraintprogramming...
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ISBN:
(纸本)9783319661582;9783319661575
A conceptual clustering is a set of formal concepts (i.e., closed itemsets) that defines a partition of a set of transactions. Finding a conceptual clustering is an NP-complete problem for which constraintprogramming (cp) and Integer Linear programming (ILP) approaches have been recently proposed. We introduce new cp models to solve this problem: a pure cp model that uses set constraints, and an hybrid model that uses a data mining tool to extract formal concepts in a preprocessing step and then uses cp to select a subset of formal concepts that defines a partition. We compare our new models with recent cp and ILP approaches on classical machine learning instances. We also introduce a new set of instances coming from a real application case, which aims at extracting setting concepts from an Enterprise Resource Planning (ERP) software. We consider two classic criteria to optimize, i.e., the frequency and the size. We show that these criteria lead to extreme solutions with either very few small formal concepts or many large formal concepts, and that compromise clusterings may be obtained by computing the Pareto front of non dominated clusterings.
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