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13th international Workshop on Rewriting logic and Its Applications, WRLA 2020

ISBN: (纸本)9783030635947

the proceedings contain 11 papers. the special focus in this conference is on Rewriting logic and Its Applications. the topics include: Automated Construction of Security Integrity Wrappers for Industry 4.0 Applications;connecting Constrained Constructor Patterns and Matching logic;Analysis of the Runtime Resource Provisioning of BPMN Processes Using Maude;a Rule-Based System for Computation and Deduction in Mathematica;variants in the Infinitary Unification Wonderland;variant Satisfiability of Parameterized Strings;inductive Reasoning with Equality Predicates, Contextual Rewriting and Variant-Based Simplification;a Simplified Application of Howard’s Vector Notation System to Termination Proofs for Typed Lambda-Calculus Systems;strategies, Model Checking and Branching-Time Properties in Maude.

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A new model for single machine scheduling with uncertain processing time

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JOURNAL OF INTELLIGENT MANUFACTURING 2017年第3期28卷 717-725页

作者： Hu, Kai Zhang, Xingfang Gen, Mitsuo Jo, Jungbok Liaocheng Univ Sch Math Sci Liaocheng 252059 Peoples R China Tokyo Univ Sci Tokyo Japan Japan & Fuzzy Log Syst Inst Iizuka Fukuoka Japan Dongseo Univ Div Comp & Informat Engn Busan South Korea

Uncertain single machine scheduling problem for batches of jobs is an important issue for manufacturing systems. In this paper, we use uncertainty theory to study the single machine scheduling problem with deadlines where the processing times are described by uncertain variables with known uncertainty distributions. A new model for this problem is built to maximize expected total weight of batches of jobs. then the model is transformed into a deterministic integer programming model by using the operational law for inverse uncertainty distributions. In addition, a property of the transformed model is provided and an algorithm is designed to solve this problem. Finally, a numerical example is given to illustrate the effectiveness of the model and the proposed algorithm.

关键词： Integer programming Uncertainty theory Batch scheduling

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Probabilistic abductive logic programming: A joint approach of logic and probability 1

Probabilistic abductive logic programming: A joint approach ...

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1st Doctoral Workshop in Artificial Intelligence, DWAI 2013, An Official Workshop of the 13th international conference of the Italian Association for Artificial Intelligence, AI*IA 2013

作者： Rotella, F. Ferilli, S. Dipartimento di Informatica Universit di Bari Italy Centro Interdipartimentale per la Logica e sue Applicazioni Universit di Bari Italy

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LearnSec: A Framework for Full Text Analysis 13th

LearnSec: A Framework for Full Text Analysis

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13th international conference on Hybrid Artificial Intelligent Systems (HAIS)

作者： Goncalves, Carlos Iglesias, E. L. Borrajo, L. Camacho, Rui Seara Vieira, A. Goncalves, Celia Talma Univ Vigo Higher Tech Sch Comp Engn Campus Univ Lagoas S-N Orense 32004 Spain FEUP U Porto Rua Dr Roberto Frias S-N P-4200465 Porto Portugal LIAAD INESC TEC Porto Portugal ISCAP P Porto Rua Jaime Lopes Amorim S-N P-4465004 Sao Mamede de Infesta Portugal U Porto LIACC Porto Portugal

ISBN: (纸本)9783319926391;9783319926384

Large corpus of scientific research papers have been available for a long time. However, most of those corpus store only the title and the abstract of the paper. For some domains this information may not be enough to achieve high performance in text mining tasks. this problem has been recently reduced by the growing availability of full text scientific research papers. A full text version provides more detailed information but, on the other hand, a large amount of data needs to be processed. A priori, it is difficult to know if the extra work of the full text analysis has a significant impact in the performance of text mining tasks, or if the effect depends on the scientific domain or the specific corpus under analysis. the goal of this paper is to show a framework for full text analysis, called LearnSec, which incorporates domain specific knowledge and information about the content of the document sections to improve the classification process with propositional and relational learning. To demonstrate the usefulness of the tool, we process a scientific corpus based on OSHUMED, generating an attribute/value dataset in Weka format and a First Order logic dataset in inductive logic programming (ILP) format. Results show a successful assessment of the framework.

关键词： Full text analyses Text preprocessing Text mining Use of background knowledge inductive logic programming

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Automating Induction with an SMT Solver

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13th international conference on Verification, Model Checking, and Abstract Interpretation

作者： Leino, K. Rustan M. Microsoft Res Redmond WA USA

ISBN: (纸本)9783642279393;9783642279409

Mechanical proof assistants have always had support for inductive proofs. Sometimes an alternative to proof assistants. satisfiability modulo theories (SMT) solvers bring the hope of a higher degree of automation. However. SMT solvers do not natively support induction, so inductive proofs require some encoding into the SMT solver's input. this paper shows a surprisingly simple tactic a rewriting strategy and a heuristic for when to apply it that has shown to be useful in verifying simple inductive theorems, like those that can occur during program verification. the paper describes the tactic and its implementation in a program verifier. and reports on the positive experience with using the tactic.

关键词： Formal logic

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13th Asian Symposium on programming Languages and Systems, APLAS 2015

13th Asian Symposium on Programming Languages and Systems, A...

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13th Asian Symposium on programming Languages and Systems, APLAS 2015

ISBN: (纸本)9783319265285

the proceedings contain 26 papers. the special focus in this conference is on Compilers and Separation logic. the topics include: Memory-efficient tail calls in the JVM with imperative functional objects;a secure compiler for ML modules;a complete and polynomial-time algorithm in SSA;separation logic with monadic inductive definitions and implicit existentials;tree-like grammars and separation logic;randomized resource-aware path-sensitive static analysis;an extension of zonotopes to quadratic arithmetics;abstraction of optional numerical values;fault-tolerant resource reasoning;a blame calculus with delimited control;aliasing control in an imperative pure calculus;a strong distillery;from call-by-value to interaction by typed closure conversion;a marriage of game semantics and operational techniques;automata-based abstraction for automated verification of higher-order tree-processing programs;decision algorithms for checking definability of order-2 finitary PCF;uncovering javascript performance code smells relevant to type mutations;analyzing distributed multi-platform java and android applications with shadowVM;quasi-linearizability is undecidable;objects in polynomial time;programming techniques for reversible comparison sorts;transactions on mergeable objects;a sound type system for layer subtyping and dynamically activated first-class layers;bottom-up context-sensitive pointer analysis for java and more sound static handling of java reflection.

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13th international conference on Algorithmic Learning theory, ALT 2002

13th International Conference on Algorithmic Learning Theory...

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13th international conference on Algorithmic Learning theory, ALT 2002

ISBN: (纸本)3540001700

the proceedings contain 32 papers. the special focus in this conference is on Learning Boolean Functions, Margin-Based Learning, Learning with Queries and inductive Inference. the topics include: On the Eigen spectrum of the gram matrix and its relationship to the operator Eigen spectrum;interim report on a musical discovery project;learning structure from sequences, with applications in a digital library;on learning monotone Boolean functions under the uniform distribution;maximizing agreements and coagnostic learning;optimally-smooth adaptive boosting and application to agnostic learning;large margin classification for moving targets;on the smallest possible dimension and the largest possible margin of linear arrangements representing given concept classes uniform distribution;a general dimension for approximately learning Boolean functions;the complexity of learning concept classes with polynomial general dimension;on the absence of predictive complexity for some games;consistency queries in information extraction;ordered term tree languages which are polynomial time inductively inferable from positive data;reflective inductive inference of recursive functions;on the learnability of vector spaces;learning, logic, and topology in a common framework;a pathology of bottom-up hill-climbing in inductive rule learning;minimised residue hypotheses in relevant logic;compactness and learning of classes of unions of erasing regular pattern languages;a negative result on inductive inference of extended pattern languages;asymptotic optimality of transductive confidence machine and an efficient PAC algorithm for reconstructing a mixture of lines.

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Branching-time temporal logic extended with qualitative presburger constraints

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13th international conference on logic for programming, Artificial Intelligence, and Reasoning

作者： Bozzelli, Laura Gascon, Regis CNRS LSV Cachan France ENS Cachan Cachan France

ISBN: (纸本)3540482814

Recently, LTL extended with atomic formulas built over a constraint language interpreting variables in Z has been shown to have a decidable satisfiability and model-checking problem. this language allows to compare the variables at different states of the model and include periodicity constraints, comparison constraints, and a restricted form of quantification. On the other hand, the CTL counterpart of this logic (and hence also its CTL* counterpart which subsumes both LTL and CTL) has an undecidable model-checking problem. In this paper, we substantially extend the decidability border, by considering a meaningful fragment of CTL* extended with such constraints (which subsumes both the universal and existential fragments, as well as the EF-like fragment) and show that satisfiability and model-checking over relational automata that are abstraction of counter machines are decidable. the correctness and the termination of our algorithm rely on a suitable well quasi-ordering defined over the set of variable valuations.

关键词： logic programming

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inductive logic programming meets relational databases: Efficient learning of markov logic networks 1

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26th international conference on inductive logic programming, ILP 2016

作者： Malec, Marcin Khot, Tushar Nagy, James Blask, Erik Natarajan, Sriraam Indiana University Bloomington BloomingtonIN United States Allen Institute of AI Seattle United States Air Force Research Laboratory Riverside United States

ISBN: (数字)9783319633428

ISBN: (纸本)9783319633411

Statistical Relational Learning (SRL) approaches have been developed to learn in presence of noisy relational data by combining probability theory with first order logic. While powerful, most learning approaches for these models do not scale well to large datasets. While advances have been made on using relational databases with SRL models [14], they have not been extended to handle the complex model learning (structure learning task). We present a scalable structure learning approach that combines the benefits of relational databases with search strategies that employ rich inductive bias from inductive logic programming. We empirically show the benefits of our approach on boosted structure learning for Markov logic Networks. © Springer international Publishing AG 2017.

关键词： Markov processes

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Algorithmic Learning theory 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Marcus Hutter Frank Stephan Vladimir Vovk thomas Zeugmann

ISBN: (数字)9783642161087

ISBN: (纸本)9783642161070

this volume contains the papers presented at the 21st international Conf- ence on Algorithmic Learning theory (ALT 2010), which was held in Canberra, Australia, October 6–8, 2010. the conference was co-located with the 13th - ternational conference on Discovery Science (DS 2010) and with the Machine Learning Summer School, which was held just before ALT 2010. the tech- cal program of ALT 2010, contained 26 papers selected from 44 submissions and ?ve invited talks. the invited talks were presented in joint sessions of both conferences. ALT 2010 was dedicated to the theoretical foundations of machine learning and took place on the campus of the Australian National University, Canberra, Australia. ALT provides a forum for high-quality talks with a strong theore- cal background and scienti?c interchange in areas such as inductive inference, universal prediction, teaching models, grammatical inference, formal languages, inductive logic programming, query learning, complexity of learning, on-line learning and relative loss bounds, semi-supervised and unsupervised learning, clustering,activelearning,statisticallearning,supportvectormachines,Vapnik- Chervonenkisdimension,probablyapproximatelycorrectlearning,Bayesianand causal networks, boosting and bagging, information-based methods, minimum descriptionlength,Kolmogorovcomplexity,kernels,graphlearning,decisiontree methods, Markov decision processes, reinforcement learning, and real-world - plications of algorithmic learning theory. DS 2010 was the 13th international conference on Discovery Science and focused on the development and analysis of methods for intelligent data an- ysis, knowledge discovery and machine learning, as well as their application to scienti?c knowledge discovery. As is the tradition, it wasco-located and held in parallel with Algorithmic Learning theory.

关键词： Artificial Intelligence programming Techniques Mathematical logic and Formal Languages Algorithm Analysis and Problem Complexity Computation by Abstract Devices logics and Meanings of Programs

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