Constraint answer set programming or CASP, for short, is a hybrid approach in automated reasoning putting together the advances of distinct research areas such as answer set programming, constraint processing, and sat...
详细信息
Constraint answer set programming or CASP, for short, is a hybrid approach in automated reasoning putting together the advances of distinct research areas such as answer set programming, constraint processing, and satisfiability modulo theories. CASP demonstrates promising results, including the development of a multitude of solvers: acsolver, clingcon, ezcsp, idp, inca, dingo, mingo, aspmt2smt, clingo[l,dl], and ezsmt. It opens new horizons for declarative programming applications such as solving complex train scheduling problems. Systems designed to find solutions to constraint answer set programs can be grouped according to their construction into, what we call, integrational or translational approaches. The focus of this paper is an overview of the key ingredients of the design of constraint answer set solvers drawing distinctions and parallels between integrational and translational approaches. The paper also provides a glimpse at the kind of programs its users develop by utilizing a CASP encoding of Traveling Salesman problem for illustration. In addition, we place the CASP technology on the map among its automated reasoning peers as well as discuss future possibilities for the development of CASP.
The shortest path problem is a classic optimization problem in graph theory and computer technology. It involves identifying the shortest path between two nodes in a graph, where each edge has a numerical weight. In t...
详细信息
Many courses in computerscience feature team projects which expose students to challenges resembling those in the software industry. For a fair assessment in team projects, measuring each student's contribution i...
详细信息
ISBN:
(纸本)9798400704239
Many courses in computerscience feature team projects which expose students to challenges resembling those in the software industry. For a fair assessment in team projects, measuring each student's contribution is a prerequisite. As version control systems store snapshots of a team's work, they can help make students' contributions transparent. However, available tools do not offer the required functionality to effectively analyze the distribution of work in a group. In this paper, we introduce Git Reporter, a new tool for measuring contributions in projects based on Git, the most widely used version control system. Git Reporter categorizes students' contributions based on importance and summarizes the distribution of work. Moreover, our tool provides a detailed mapping from each part of the project to its respective author. We found that using Git Reporter helps teaching assistants evaluate the quality of students' contributions. Furthermore, it significantly increases teaching assistants' confidence in their grading decision when compared with Git or a popular tool based on Git. A survey conducted among students indicates that Git Reporter may raise group awareness in student teams and help them divide the work more equally. Git Reporter is available as an open-source tool supporting assessment and team work. Furthermore, Git Reporter can support research in computing education and software repository mining by providing improved metrics compared to existing tools.
In this paper, we conduct numerical experiments to test the effectiveness of several integer programming formulations of the cycle selection problem. Specifically, we carry out experiments to identify a maximum weight...
详细信息
ISBN:
(纸本)9783031609237;9783031609244
In this paper, we conduct numerical experiments to test the effectiveness of several integer programming formulations of the cycle selection problem. Specifically, we carry out experiments to identify a maximum weighted cycle selection in random or in structured digraphs. The results show that random instances are relatively easy and that two formulations outperform the other ones in terms of total running time. We also examine variants of the problem obtained by adding a budget constraint and/or a maximum cycle length constraint. These variants are more challenging, especially when a budget constraint is imposed. To investigate the cycle selection problem with a maximum cycle length equal to 3, we provide an arc-based formulation with an exponential number of constraints that can be separated in polynomial time. All inequalities in the formulation are facet-defining for complete digraphs.
作者：
Mallach, SvenUniv Bonn
High Performance Comp & Analyt Lab Friedrich Hirzebruch Allee 8 D-53115 Bonn Germany
We present a family of integer programming formulations for the maximum cut problem. These formulations encode the incidence vectors of the cuts of a connected graph by employing a subset of the odd-cycle inequalities...
详细信息
ISBN:
(纸本)9783031609237;9783031609244
We present a family of integer programming formulations for the maximum cut problem. These formulations encode the incidence vectors of the cuts of a connected graph by employing a subset of the odd-cycle inequalities that relate to a spanning tree, and they require only the corresponding edge variables to be integral explicitly. They so describe sufficient restrictions of the classic integer linear program by Barahona and Mahjoub. In addition, we characterize according formulations comprising facet-defining inequalities only. Trade-offs and comparisons to prevalent formulations concerning size and relaxation strength are subject to an experimental study.
Australian CS programs generally provide good problem-solving skills, depth in the foundations of computing theory, hardware and software operating environments and technologies, and complexity, together with a strong...
详细信息
Besides training, mathematical optimization is also used in deep learning to model and solve formulations over trained neural networks for purposes such as verification, compression, and optimization with learned cons...
详细信息
ISBN:
(数字)9783031605994
ISBN:
(纸本)9783031606014;9783031605994
Besides training, mathematical optimization is also used in deep learning to model and solve formulations over trained neural networks for purposes such as verification, compression, and optimization with learned constraints. However, solving these formulations soon becomes difficult as the network size grows due to the weak linear relaxation and dense constraint matrix. We have seen improvements in recent years with cutting plane algorithms, reformulations, and an heuristic based on Mixed-Integer Linear programming (MILP). In this work, we propose a more scalable heuristic based on exploring global and local linear relaxations of the neural network model. Our heuristic is competitive with a state-of-the-art MILP solver and the prior heuristic while producing better solutions with increases in input, depth, and number of neurons.
Smart manufacturing requires easily reconfigurable robotic systems to increase the flexibility in presence of market uncertainties by reducing the set-up times for new tasks. One enabler of fast reconfigurability is g...
详细信息
Smart manufacturing requires easily reconfigurable robotic systems to increase the flexibility in presence of market uncertainties by reducing the set-up times for new tasks. One enabler of fast reconfigurability is given by intuitive robot programming methods. On the one hand, offline skill-based programming (OSP) allows the definition of new tasks by sequencing pre-defined, parameterizable building blocks termed as skills in a graphical user interface. On the other hand, programming by demonstration (PbD) is a well known technique that uses kinesthetic teaching for intuitive robot programming, where this work presents an approach to automatically recognize skills from the human demonstration and parameterize them using the recorded data. The approach further unifies both programming modes of OSP and PbD with the help of an ontological knowledge base and empowers the end user to choose the preferred mode for each phase of the task. In the experiments, we evaluate two scenarios with different sequences of programming modes being selected by the user to define a task. In each scenario, skills are recognized by a data-driven classifier and automatically parameterized from the recorded data. The fully defined tasks consist of both manually added and automatically recognized skills and are executed in the context of a realistic industrial assembly environment. (c) 2024 The Authors. Published by Elsevier B.V.
We present results of an in-depth survey of nearly 100 introductory programming (CS1) instructors in 18 countries spanning six continents. Although CS1 is well studied, relatively few broadly-scoped studies have been ...
详细信息
ISBN:
(纸本)9798400704239
We present results of an in-depth survey of nearly 100 introductory programming (CS1) instructors in 18 countries spanning six continents. Although CS1 is well studied, relatively few broadly-scoped studies have been conducted, and none prior have exceeded regional scale. In addition, CS1 is a notoriously fickle and often changing course, and many might find it beneficial to know what other instructors are doing across the globe;perhaps more so as we continue to understand the impact of the COVID-19 pandemic on computing education and as the effects of Generative AI take hold. Expanding upon several surveys conducted in Australasia, the UK, and Ireland, this survey facilitates a direct comparison of global trends in CS1. The survey goes beyond environmental factors such as languages used, and examines why CS1 instructors teach what they do, in the ways they do. In total the survey spans 84 institutions and 91 courses in which a total of over 40,000 students are enrolled.
Real-time collaborative programming enables a group of programmers to edit shared source code at the same time, which significantly complements the traditional non-real-time collaborative programming supported by vers...
详细信息
暂无评论