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15th International Workshop on Neural-Symbolic Learning and Reasoning (NeSy) as part of the 1st International Joint Conference on Learning and Reasoning (IJCLR)

作者： Dragone, Paolo Teso, Stefano Passerini, Andrea Twitter London England Univ Trento Trento Italy

We propose Nester, a method for injecting neural networks into constrained structured predictors. Nester first uses a neural network to compute an initial prediction that may or may not satisfy the constraints, and then applies a constraint-based structured predictor to refine the raw predictions according to hard and soft constraints. Nester combines the advantages of its two components: the network can learn complex representations from low-level data while the constraint program on top reasons about the high-level properties and requirements of the prediction task. An empirical evaluation on handwritten equation recognition shows that Nester achieves better performance than both the either component in isolation, especially when training examples are scarce, while scaling to more complex problems than other neuro-programming approaches. Nester proves especially useful to reduce errors at the semantic level of the problem, which is particularly challenging for neural network architectures.

关键词： Machine Learning Neuro-Symbolic Integration Structured Prediction constraint programming

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Scheduling of Mobile Robots Using constraint programming 25th

Scheduling of Mobile Robots Using Constraint Programming

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25th International Conference on the Principles and Practice of constraint programming (CP)

作者： Murin, Stanislav Rudova, Hana Masaryk Univ Fac Informat Brno Czech Republic

ISBN: (纸本)9783030300487

Mobile robots in flexible manufacturing systems can transport components for jobs between machines as well as process jobs on selected machines. While the job shop problem with transportation resources allows encapsulating of transportation, this work concentrates on the extended version of the problem, including the processing by mobile robots. We propose a novel constraint programming model for this problem where the crucial part of the model lies in a proper inclusion of the transportation. We have implemented it in the Optimization programming Language using the CP Optimizer, and compare it with the existing mixed integer programming solver. While both approaches are capable of solving the problem optimally, a new constraint programming approach works more efficiently, and it can compute solutions in more than an order of magnitude faster. Given that, the results of more realistic data instances are delivered in real-time, which is very important in a smart factory.

关键词： Scheduling constraint programming Mobile robot Flexible manufacturing system Transportation IBM ILOG CPLEX Optimization Studio

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Using constraint programming techniques to improve incident management process in ITIL 3

Using constraint programming techniques to improve incident ...

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3rd International Conference on Artificial Intelligence and Pattern Recognition (AIPR)

作者： Ghrab, Ismail Ketata, Mounir Loukil, Zied Gargouri, Faiez Univ Sfax MIRACL Sfax Tunisia

ISBN: (纸本)9781467391870

ITIL is a large collection of best practices, tools and methods used in the management and handling of IT services. It's composed of five books related to the most important IT management fields. In this paper, we will place the emphasis on the Service Operation field in ITIL and more precisely, on the incident management process used for managing the life cycle of IT incidents. The main idea is to find a solution for an automated optimal planning of interventions in the incident management process. Indeed, despite the number of software solutions for incident management process, intervention planning is still a manual task due to the high complexity of its automation. In this paper, we propose two solutions for automated optimal planning of interventions in the incident management. The first is inspired by the vehicle routing problems and the second is inspired by the constraint-based problems. Eventually, we will compare between the results of these two solution.

关键词： ITIL Incident management intervention planning constraint programming VRP

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A multi-scale constraint programming model of alternative splicing regulation

A multi-scale constraint programming model of alternative sp...

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1st International Workshop on Computational Methods in Systems Biology

作者： Eveillard, D Ropers, D de Jong, H Branlant, C Bockmayr, A Univ Henri Poincare LORIA F-54506 Vandoeuvre Les Nancy France UHP CNRS UMR 7567 Lab Maturat ARN & Enzymol Mol F-54506 Vandoeuvre Les Nancy France INRIA Rhone Alpes F-38334 Saint Ismier France

Alternative splicing is a key process in post-transcriptional regulation, by which different mature RNA can be obtained from the same premessenger RNA. The resulting combinatorial complexity contributes to biological diversity, especially in the case of the human immunodeficiency virus HIV-1. Using a constraint programming approach, we develop a model of the alternative splicing regulation in HIV-1. Our model integrates different scales (single site vs. multiple sites), and thus allows us to exploit several types of experimental data available to us. (C) 2004 Elsevier B.V. All rights reserved.

关键词： computational biology constraint programming modeling hybrid system alternative splicing

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Introducing ADegree: Anonymisation of Social Networks Through constraint programming 1st

Introducing ADegree: Anonymisation of Social Networks Throug...

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1st International Workshop on Mobility Analytics for Spatiotemporal and Social Data (MATES)

作者： Solonets, Sergei Drobny, Victor Rivera, Victor Lee, JooYoung Innopolis Univ Inst Technol & Software Dev Innopolis Russia

ISBN: (纸本)9783319735214;9783319735207

With the rapid growth of Online Social Networks (OSNs) and the information involved in them, research studies concerning OSNs, as well as the foundation of businesses, have become popular. Privacy on OSNs is typically protected by anonymisation methods. Current methods are not sufficient to ensure privacy and they impose restrictions on the network making it not suitable for research studies. This paper introduces an approach to find an optimal anonymous graph under user-defined metrics using constraint programming, a technique that provides well-tested and optimised engine for combinatorial problems. The approach finds a good trade-off between protection of sensitive data and quality of the information represented by the network.

关键词： Anonymisation Online social networks constraint programming

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Scheduling elective surgeries with sequence-dependent setup times to multiple operating rooms using constraint programming

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OPERATIONS RESEARCH FOR HEALTH CARE 2014年第3期3卷 160-167页

作者： Zhao, Zhaoxia Li, Xueping Univ Tennessee Dept Ind & Syst Engn 522 John D Tickle Bldg851 Neyland Dr Knoxville TN 37996 USA

The problem studied in this paper is to schedule elective surgeries (in contrast to urgent surgeries) to multiple operating rooms (ORs) in ambulatory surgical settings. We focus on three aspects of the daily scheduling decisions, including the number of ORs to open, the allocation of surgery-to-OR, and the sequence of surgeries in each OR. All the surgeries to be scheduled are known in advance, which is a common assumption for elective surgery scheduling problems. The surgeries belong to different types, and each OR can only allow certain types of surgeries to be performed. Before a surgery starts, some setup work needs to be done, such as sterilization and preparing required equipment. The setup times are assumed sequence-dependent, and both setup times and surgery durations are deterministic. The fixed costs of running the ORs are high;while sometimes overtime costs, which are even higher than the fixed costs, may occur when the surgeries cannot be done within the normal operating period of the ORs. We build a Mixed Integer Nonlinear programming (MINLP) model and a constraint programming (CP) model to solve this problem. The performance of these two models is tested on numerical examples, and the results show that the CP model is more efficient than the MINLP model in terms of the computational time and solution quality. We also examine the sensitivity of the solutions to the variation of surgery durations, and the analysis shows that the total costs do not change much when the variations of surgery durations are small. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Elective surgery scheduling Multiple operating rooms Sequence-dependent setup time constraint programming Mixed integer nonlinear programming

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Solving the Traveling Tournament Problem with Predefined Venues by Parallel constraint programming 1

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6th International Conference on Mining Intelligence and Knowledge Exploration (MIKE)

作者： Liu, Ke Loeffler, Sven Hofstedt, Petra Brandenburg Univ Technol Cottbus Senftenberg Dept Math & Comp Sci Konrad Wachsmann Allee 5 D-03044 Cottbus Germany

ISBN: (数字)9783030059187

ISBN: (纸本)9783030059187;9783030059170

The Traveling Tournament Problem with Predefined Venues (TTPPV) is a practical problem arising from sports scheduling. We describe two different modeling approaches for this problem, each of which is suitable for different sizes of instance. The experimental results show that our modeling approaches lead to improved performance compared to previous techniques in terms of the number of feasible solutions and the optimal value. Furthermore, we present how to execute the models in parallel through data-level parallelism. The parallel versions do not only gain speedup but also attain significant improvement on optimal value since more subtrees are searched independently.

关键词： Sports scheduling constraint programming Parallel constraint solving TTPPV

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Genetic-based constraint programming for Resource Constrained Job Scheduling

Genetic-based Constraint Programming for Resource Constraine...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Nguyen, Su Thiruvady, Dhananjay Sun, Yuan Zhang, Mengjie RMIT Univ Sch Accounting Informat Syst & Supply Chain Melbourne Vic Australia Deakin Univ Sch Informat Technol Melbourne Vic Australia La Trobe Univ La Trobe Business Sch Melbourne Vic Australia Victoria Univ Wellington Ctr Data Sci & Artificial Intelligence Wellington New Zealand

ISBN: (纸本)9798400704949

Resource constrained job scheduling is a hard combinatorial optimisation problem that originates in the mining industry. Off-the-shelf solvers cannot solve this problem satisfactorily in reasonable time-frames, while other solution methods such as evolutionary computation methods and matheuristics cannot guarantee optimality and require low-level customisation and specialised heuristics to be effective. This paper addresses this gap by proposing a genetic programming algorithm to discover efficient search strategies of constraint programming for resource-constrained job scheduling. In the proposed algorithm, evolved programs represent variable selectors to be used in the search process of constraint programming, and their fitness is determined by the quality of solutions obtained by constraint programming for training instances. The novelties of this algorithm are (1) a new representation of variable selectors, (2) a new fitness evaluation scheme, and (3) a pre-selection mechanism. Tests with a large set of random and benchmark instances show that the evolved variable selectors can significantly improve the efficiency of constraining programming. Compared to highly customised metaheuristics and hybrid algorithms, evolved variable selectors can help constraint programming identify quality solutions faster and proving optimality is possible if sufficiently large run-times are allowed. The evolved variable selectors are especially helpful when solving instances with large numbers of machines.

关键词： Genetic programming Job scheduling constraint programming

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On the generalization of constraint programming and boolean satisfiability solving techniques to schedule a resource-constrained project consisting of multi-mode jobs

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OPERATIONS RESEARCH PERSPECTIVES 2017年 4卷 1-11页

作者： Schnell, Alexander Hartl, Richard F. Univ Vienna Dept Business Adm Chair Prod & Operat Management Oskar Morgenstern Pl 1 A-1090 Vienna Austria

In our paper, we analyze new exact approaches for the multi-mode resource-constrained project scheduling (MRCPSP) problem with the aim of makespan minimization. For the single-mode RCPSP (SRCPSP) recent exact algorithms combine a Branch and Bound algorithm with principles from constraint programming (CP) and Boolean Satisfiability Solving (SAT). We extend the above principles for the solution of MRCPSP instances. This generalization is on the one hand achieved on the modeling level. We propose three CP-based formulations of the MRCPSP for the G12 CP platform and the optimization framework SCIP which both provide solution techniques combining CP and SAT principles. For one of the latter we implemented a new global constraint for SCIP, which generalizes the domain propagation and explanation generation principles for renewable resources in the context of multi-mode jobs. Our constraint applies the above principles in a more general way than the existing global constraint in SCIP. We compare our approaches with the state-of-the-art exact algorithm from the literature on MRCPSP instances with 20 and 30 jobs. Our computational experiments show that we can outperform the latter approach on these instances. Furthermore, we are the first to close (find the optimal solution and prove its optimality for) 628 open instances with 50 and 100 jobs from the literature. In addition, we improve the best known lower bound of 2815 instances and the best known upper bound of 151 instances. (C) 2017 The Authors. Published by Elsevier Ltd.

关键词： Multi-mode resource-constrained project scheduling constraint programming SAT solving SCIP Lazy clause generation Exact algorithm

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Component Based Design using constraint programming for Module Placement on FPGAs

Component Based Design using Constraint Programming for Modu...

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8th International Workshop on Reconfigurable and Communication-Centric Systems-on-Chip (ReCoSoC)

作者： Wold, Alexander Koch, Dirk Torresen, Jim Univ Oslo Dept Informat N-0316 Oslo Norway

ISBN: (纸本)9781467361804

constraint satisfaction modeling is both an efficient, and an elegant approach to model and solve many real world problems. In this paper, we present a constraint solver targeting module placement in static and partial run-time reconfigurable systems. We use the constraint solver to compute feasible placement positions. Our placement model incorporates communication, implementation variants and device configuration granularity. In addition, we model heterogeneous resources such as embedded memory, multipliers and logic. Furthermore, we take into account that logic resources consist of different types including logic only LUTs, arithmetic LUTs with carry chains, and LUTs with distributed memory. Our work targets state of the art field-programmable gate arrays (FPGAs) in both design-time and run-time applications. In order to evaluate our placement model and module placer implementation, we have implemented a repository containing 200 fully functional, placed and routed relocatable modules. The modules are used to implement complete systems. This validates the feasibility of both the model and the module placer. Furthermore, we present simulated results for run-time applications, and compare this to other state of the art research. In run-time applications, the results point to improved resource utilization. This is a result of using a finer tile grid and complex module shapes.

关键词： constraint programming FPGA relocatable modules reconfigurable architectures partial run-time reconfiguration.

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