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检索条件"任意字段=36th International Conference on Logic Programming, ICLP 2020"

共 105 条记录，以下是81-90 订阅

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Introducing Constraint Well-Founded Semantics for Constraint logic programming through Rewriting Transformations

Introducing Constraint Well-Founded Semantics for Constraint...

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international conference on Tools for Artificial Intelligence (ICTAI)

作者： Bryan Garreau Martin Dieguez Lodeiro Eric Monfroy Igor Stéphan Université d'Angers Angers France

ISBN: (数字)9798331527235

ISBN: (纸本)9798331527242

Constraint Answer Set programming (CASP) combines Answer Set programming (ASP) and Constraint programming (CP) to offer a powerful framework for solving complex problems. While there exists various ways to represent CASP problems, fusing both in a way that benefits both communities remains difficult to this day. To address this, we propose to extend the Well Founded Semantics (WFS) to propositional constraint logic programs. To this end, we present an iterative process involving elementary rewriting transformations to simplify the program iteratively. Our method aims to give a better description of the interactions between ASP and CP inside of a constraint logic program.

关键词： Constraint handling Semantics Answer set programming logic Iterative methods Artificial intelligence

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Polynomial time algorithms in invariant theory for torus actions 21

Polynomial time algorithms in invariant theory for torus act...

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36th Computational Complexity conference, CCC 2021

作者： Bürgisser, Peter Doğan, M. Levent Makam, Visu Walter, Michael Wigderson, Avi Institut für Mathematik Technische Universität Berlin Germany Institute for Advanced Study PrincetonNJ Canada School of Mathematics and Statistics University of Melbourne Australia Korteweg-de Vries Institute for Mathematics Institute for Theoretical Physics Institute for Logic Language and Computation University of Amsterdam Netherlands

ISBN: (纸本)9783959771931

An action of a group on a vector space partitions the latter into a set of orbits. We consider three natural and useful algorithmic "isomorphism" or "classification" problems, namely, orbit equality, orbit closure intersection, and orbit closure containment. these capture and relate to a variety of problems within mathematics, physics and computer science, optimization and statistics. these orbit problems extend the more basic null cone problem, whose algorithmic complexity has seen significant progress in recent years. In this paper, we initiate a study of these problems by focusing on the actions of commutative groups (namely, tori). We explain how this setting is motivated from questions in algebraic complexity, and is still rich enough to capture interesting combinatorial algorithmic problems. While the structural theory of commutative actions is well understood, no general efficient algorithms were known for the aforementioned problems. Our main results are polynomial time algorithms for all three problems. We also show how to efficiently find separating invariants for orbits, and how to compute systems of generating rational invariants for these actions (in contrast, for polynomial invariants the latter is known to be hard). Our techniques are based on a combination of fundamental results in invariant theory, linear programming, and algorithmic lattice theory. © Peter Bürgisser, M. Levent Doğan, Visu Makam, Michael Walter, and Avi Wigderson

关键词： Vector spaces

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Linear Algebraic Partial Evaluation of logic Programs

Linear Algebraic Partial Evaluation of Logic Programs

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international conference on Tools for Artificial Intelligence (ICTAI)

作者： Tuan Nguyen Katsumi Inoue Chiaki Sakama National Institute of Informatics Tokyo Japan Wakayama University Wakayama Japan

ISBN: (数字)9798331527235

ISBN: (纸本)9798331527242

In logic programming, partial evaluation (PE) performs unfolding rules in advance to reduce the cost of inferencing. Recently, PE of logic programs has been implemented in vector spaces by computing the powers of matrix representations. It has been reported that linear algebraic PE substantially enhances the practical performance of linear algebraic methods for logic programming. However, most recent research has focused exclusively on And-rules, assuming that their dependency graph is acyclic. In this paper, we introduce cycle-resolving techniques to ensure that linear algebraic PE works effectively even with cycles in the program. Additionally, we demonstrate that linear algebraic PE can also be extended to accommodate Or-rules. Moreover, we propose using eigendecomposition and Jordan normal form to conduct PE in vector spaces. We compare the proposed techniques on a set of acyclic and cyclic logic programs to evaluate their effectiveness. It is shown that the iteration method for PE, especially with sparse format, is the most efficient one in general cases. However, the decomposition method has the potential for future research to leverage eigenvalues and eigenvectors of program matrices for reasoning.

关键词： Costs logic programming Focusing Eigenvalues and eigenfunctions Vectors Cognition Graph theory logic Matrix decomposition Sparse matrices

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VAEL: bridging variational autoencoders and probabilistic logic programming 22

VAEL: bridging variational autoencoders and probabilistic lo...

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Proceedings of the 36th international conference on Neural Information Processing Systems

作者： Eleonora Misino Giuseppe Marra Emanuele Sansone Department of Computer Science and Engineering University of Bologna Italy Department of Computer Science KU Leuven Belgium

ISBN: (纸本)9781713871088

We present VAEL, a neuro-symbolic generative model integrating variational autoencoders (VAE) with the reasoning capabilities of probabilistic logic (L) programming. Besides standard latent subsymbolic variables, our model exploits a probabilistic logic program to define a further structured representation, which is used for logical reasoning. the entire process is end-to-end differentiable. Once trained, VAEL can solve new unseen generation tasks by (i) leveraging the previously acquired knowledge encoded in the neural component and (ii) exploiting new logical programs on the structured latent space. Our experiments provide support on the benefits of this neuro-symbolic integration both in terms of task generalization and data efficiency. To the best of our knowledge, this work is the first to propose a general-purpose end-to-end framework integrating probabilistic logic programming into a deep generative model.

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QDiff: Differential Testing of Quantum Software Stacks 21

QDiff: Differential Testing of Quantum Software Stacks

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IEEE international conference on Automated Software Engineering (ASE)

作者： Jiyuan Wang Qian Zhang Guoqing Harry Xu Miryung Kim University of California Los Angeles CA United States

ISBN: (纸本)9781665447843

Over the past few years, several quantum software stacks (QSS) have been developed in response to rapid hardware advances in quantum computing. A QSS includes a quantum programming language, an optimizing compiler that translates a quantum algorithm written in a high-level language into quantum gate instructions, a quantum simulator that emulates these instructions on a classical device, and a software controller that sends analog signals to a very expensive quantum hardware based on quantum circuits. In comparison to traditional compilers and architecture simulators, QSSes are difficult to tests due to the probabilistic nature of results, the lack of clear hardware specifications, and quantum programming *** work devises a novel differential testing approach for QSSes, named QDiff with three major innovations: (1) We generate input programs to be tested via semantics-preserving, source to source transformation to explore program variants. (2) We speed up differential testing by filtering out quantum circuits that are not worthwhile to execute on quantum hardware by analyzing static characteristics such as a circuit depth, 2-gate operations, gate error rates, and T1 relaxation time. (3) We design an extensible equivalence checking mechanism via distribution comparison functions such as Kolmogorov–Smirnov test and cross *** evaluate QDiff with three widely-used open source QSSes: Qiskit from IBM, Cirq from Google, and Pyquil from Rigetti. By running QDiff on both real hardware and quantum simulators, we found several critical bugs revealing potential instabilities in these platforms. QDiff’s source transformation is effective in producing semantically equivalent yet not-identical circuits (i.e., 34% of trials), and its filtering mechanism can speed up differential testing by 66%.

关键词： Technological innovation Quantum algorithm Filtering logic gates programming Hardware Software

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Learning Constraint Grammar-style disambiguation rules using inductive logic programming 98

Learning Constraint Grammar-style disambiguation rules using...

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Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics and 17th international conference on Computational Linguistics - Volume 2

作者： Nikolaj Lindberg Martin Eineborg Centre for Speech Technology Royal Institute of Technology Stockholm Sweden Telia Research AB Spoken Language Processing Haninge Sweden

this paper reports a pilot study, in which Constraint Grammar inspired rules were learnt using the Progol machine-learning system. Rules discarding faulty readings of ambiguously tagged words were learnt for the part of speech tags of the Stockholm-Umeå Corpus. Several thousand disambiguation rules were induced. When tested on unseen data, 98% of the words retained the correct reading after tagging. However, there were ambiguities pending after tagging, on an average 1.13 tags per word. the results suggest that the Progol system can be useful for learning tagging rules of good quality.

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Assessing the Impact of Psychosocial Factors on the Behavior of Diabetic Patients with Neuropathy 8th

Assessing the Impact of Psychosocial Factors on the Behavior...

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8th international conference on Information and Communication Technology for Competitive Strategies, ICTCS 2023

作者： Vicente, Henrique Portela, Manuel Ávidos, Liliana Neves, João Marreiros, Goreti Neves, José Departamento de Química e Bioquímica Escola de Ciências e Tecnologia REQUIMTE/LAQV Universidade de Évora Évora Portugal Centro Algoritmi/LASI Universidade do Minho Braga Portugal Instituto Politécnico de Saúde do Norte CESPU Famalicão Portugal Mediclinic Arabian Ranches Dubai United Arab Emirates Departamento de Engenharia Informática Instituto Superior de Engenharia do Porto Porto Portugal

ISBN: (纸本)9789819707430

Diabetic neuropathy is a prevalent consequence of diabetes, impacting as many as half of those diagnosed with the condition. this ailment results from nerve damage, manifesting as sensory and motor symptoms such as tingling, numbness, and discomfort in the extremities, especially the hands and feet. Diabetic neuropathy considerably diminishes the patient’s quality of life, prompting both physical and emotional upheaval and consequently altering their behaviors and daily routines. this study aimed to achieve two main goals: first, to understand the role played by psychosocial factors in influencing the actions of those with diabetes and neuropathy, and second, to determine the relationship between a patient’s psychosocial factors and their responses under these circumstances. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： logic programming

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Query-driven Repair of Functional Dependency Violations

Query-driven Repair of Functional Dependency Violations

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international conference on Data Engineering

作者： Stella Giannakopoulou Manos Karpathiotakis Anastasia Ailamaki EPFL Facebook

ISBN: (数字)9781728129037

ISBN: (纸本)9781728129044

Data cleaning is a time-consuming process that depends on the data analysis that users perform. Existing solutions treat data cleaning as a separate offline process that takes place before analysis begins. Applying data cleaning before analysis assumes a priori knowledge of the inconsistencies and the query workload, thereby requiring effort on understanding and cleaning the data that is unnecessary for the analysis. We propose an approach that performs probabilistic repair of functional dependency violations on-demand, driven by the exploratory analysis that users perform. We introduce Daisy, a system that seamlessly integrates data cleaning into the analysis by relaxing query results. Daisy executes analytical query-workloads over dirty data by weaving cleaning operators into the query plan. Our evaluation shows that Daisy adapts to the workload and outperforms traditional offline cleaning on both synthetic and real-world workloads.

关键词： Cleaning Maintenance engineering Probabilistic logic Tools Task analysis Optimization Query processing

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Pruning Boolean d-DNNF Circuits through Tseitin-Awareness

Pruning Boolean d-DNNF Circuits Through Tseitin-Awareness

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international conference on Tools for Artificial Intelligence (ICTAI)

作者： Vincent Derkinderen KU Leuven Belgium

ISBN: (数字)9798331527235

ISBN: (纸本)9798331527242

Boolean circuits in d-DNNF form enable tractable probabilistic inference. However, as a key insight of this work, we show that commonly used d-DNNF compilation approaches introduce irrelevant subcircuits. We call these subcircuits Tseitin artifacts, as they are introduced due to the Tseitin transformation step – a well-established procedure to transform any circuit into the CNF format required by several d-DNNF knowledge compilers. We discuss how to detect and remove both Tseitin variables and Tseitin artifacts, leading to more succinct circuits. We empirically observe an average size reduction of 77.5% when removing both Tseitin variables and artifacts. the additional pruning of Tseitin artifacts reduces the size by 22.2% on average. this significantly improves downstream tasks that benefit from a more succinct circuit, e.g., probabilistic inference tasks.

关键词： Circuits Transforms Knowledge representation Probabilistic logic Artificial intelligence

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GALOIS: boosting deep reinforcement learning via generalizable logic synthesis 22

GALOIS: boosting deep reinforcement learning via generalizab...

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Proceedings of the 36th international conference on Neural Information Processing Systems

作者： Yushi Cao Zhiming Li Tianpei Yang Hao Zhang Yan Zheng Yi Li Jianye Hao Yang Liu Nanyang Technological University Singapore College of Intelligence and Computing Tianjin university Tianjin China and University of Alberta Canada College of Intelligence and Computing Tianjin university Tianjin China

ISBN: (纸本)9781713871088

Despite achieving superior performance in human-level control problems, unlike humans, deep reinforcement learning (DRL) lacks high-order intelligence (e.g., logic deduction and reuse), thus it behaves ineffectively than humans regarding learning and generalization in complex problems. Previous works attempt to directly synthesize a white-box logic program as the DRL policy, manifesting logic-driven behaviors. However, most synthesis methods are built on imperative or declarative programming, and each has a distinct limitation, respectively. the former ignores the cause-effect logic during synthesis, resulting in low generalizability across tasks. the latter is strictly proof-based, thus failing to synthesize programs with complex hierarchical logic. In this paper, we combine the above two paradigms together and propose a novel Generalizable Logic Synthesis (GALOIS) framework to synthesize hierarchical and strict cause-effect logic programs. GALOIS leverages the program sketch and defines a new sketch-based hybrid program language for guiding the synthesis. Based on that, GALOIS proposes a sketch-based program synthesis method to automatically generate white-box programs with generalizable and interpretable cause-effect logic. Extensive evaluations on various decision-making tasks with complex logic demonstrate the superiority of GALOIS over mainstream baselines regarding the asymptotic performance, generalizability, and great knowledge reusability across different environments.

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