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arXiv 2023年

作者： Ye, Lina Khmelnitsky, Igor Haddad, Serge Barbot, Benoît Bollig, Benedikt Leucker, Martin Neider, Daniel Roy, Rajarshi Université Paris-Saclay CNRS ENS Paris-Saclay CentraleSupélec LMF France Université Paris-Saclay CNRS ENS Paris-Saclay LMF France Université Paris-Est Créteil France Institute for Software Engineering and Programming Languages Universität zu Lübeck Germany TU Dortmund University Germany Center for Trustworthy Data Science and Security University Alliance Ruhr Germany Max Planck Institute for Software Systems Kaiserslautern Germany

Angluin's L∗ algorithm learns the minimal deterministic finite automaton (DFA) of a regular language using membership and equivalence queries. Its probabilistic approximatively correct (PAC) version substitutes an equivalence query by numerous random membership queries to get a high level confidence to the answer. Thus it can be applied to any kind of device and may be viewed as an algorithm for synthesizing an automaton abstracting the behavior of the device based on observations. Here we are interested on how Angluin's PAC learning algorithm behaves for devices which are obtained from a DFA by introducing some noise. More precisely we study whether Angluin's algorithm reduces the noise and produces a DFA closer to the original one than the noisy device. We propose several ways to introduce the noise: (1) the noisy device inverts the classification of words w.r.t. the DFA with a small probability, (2) the noisy device modifies with a small probability the letters of the word before asking its classification w.r.t. the DFA, (3) the noisy device combines the classification of a word w.r.t. the DFA and its classification w.r.t. a counter automaton, and (4) the noisy DFA is obtained by a random process from two DFA such that the language of the first one is included in the second one. Then when a word is accepted (resp. rejected) by the first (resp. second) one, it is also accepted (resp. rejected) and in the remaining cases, it is accepted with probability 0.5. Our main experimental contributions consist in showing that: (1) Angluin's algorithm behaves well whenever the noisy device is produced by a random process, (2) but poorly with a structured noise, and, that (3) is able to eliminate pathological behaviours specified in a regular way. Theoretically, we show that randomness almost surely yields systems with non-recursively enumerable languages. © 2023, CC BY.

关键词： Random processes

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Analyzing Robustness of Angluin’s L∗ Algorithm in Presence of Noise

arXiv

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arXiv 2022年

作者： Khmelnitsky, Igor Haddad, Serge Ye, Lina Barbot, Benoît Bollig, Benedikt Leucker, Martin Neider, Daniel Roy, Rajarshi Université Paris-Saclay CNRS ENS Paris-Saclay INRIA LMF France Université Paris-Saclay CNRS ENS Paris-Saclay CentraleSupélec LMF France Université Paris-Est Créteil France Université Paris-Saclay CNRS ENS Paris-Saclay LMF France Institute for Software Engineering and Programming Languages Universität zu Lübeck Germany Carl von Ossietzky University of Oldenburg Germany Max Planck Institute for Software Systems Kaiserslautern Germany

Angluin’s L∗ algorithm learns the minimal (complete) deterministic finite automaton (DFA) of a regular language using membership and equivalence queries. Its probabilistic approximatively correct (PAC) version substitutes an equivalence query by a large enough set of random membership queries to get a high level confidence to the answer. Thus it can be applied to any kind of (also non-regular) device and may be viewed as an algorithm for synthesizing an automaton abstracting the behavior of the device based on observations. Here we are interested on how Angluin’s PAC learning algorithm behaves for devices which are obtained from a DFA by introducing some noise. More precisely we study whether Angluin’s algorithm reduces the noise and produces a DFA closer to the original one than the noisy device. We propose several ways to introduce the noise: (1) the noisy device inverts the classification of words w.r.t. the DFA with a small probability, (2) the noisy device modifies with a small probability the letters of the word before asking its classification w.r.t. the DFA, and (3) the noisy device combines the classification of a word w.r.t. the DFA and its classification w.r.t. a counter automaton. Our experiments were performed on several hundred DFAs. Our main contributions, bluntly stated, consist in showing that: (1) Angluin’s algorithm behaves well whenever the noisy device is produced by a random process, (2) but poorly with a structured noise, and, that (3) almost surely randomness yields systems with non-recursively enumerable languages. © 2022, CC BY.

关键词： Learning algorithms

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Automated Computation of Therapies Using Failure Mode and Effects Analysis in the Medical Domain

arXiv

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arXiv 2024年

作者： Luttermann, Malte Baake, Edgar Bouchagiar, Juljan Gebel, Benjamin Grüning, Philipp Manikwadura, Dilini Schollemann, Franziska Teifke, Elisa Rostalski, Philipp Möller, Ralf Institute of Information Systems University of Lübeck Germany Institute of Telematics University of Lübeck Germany Institute for Software Engineering and Programming Languages University of Lübeck Germany Department of Infectious Diseases and Microbiology University Hospital Schleswig-Holstein Campus Lübeck Germany Institute for Neuro- and Bioinformatics University of Lübeck Germany Institute for Molecular Medicine University of Lübeck Germany Institute for Electrical Engineering in Medicine University of Lübeck Germany franziska.schollemann Department of Anesthesiology and Intensive Care University Hospital Schleswig-Holstein Campus Lübeck Germany

Failure mode and effects analysis (FMEA) is a systematic approach to identify and analyse potential failures and their effects in a system or process. The FMEA approach, however, requires domain experts to manually analyse the FMEA model to derive risk-reducing actions that should be applied. In this paper, we provide a formal framework to allow for automatic planning and acting in FMEA models. More specifically, we cast the FMEA model into a Markov decision process which can then be solved by existing solvers. We show that the FMEA approach can not only be used to support medical experts during the modelling process but also to automatically derive optimal therapies for the treatment of patients. © 2024, CC BY.

关键词： Patient treatment

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Preface

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2004年 2981卷 V-VI页

作者： Müller-Schloer, Christian Ungerer, Theo Bauer, Bernhard University of Hannover Institute of Systems Engineering System and Computer Architecture - SRA Appelstr. 4 Hannover Germany University of Augsburg Institute of Informatics Augsburg Germany University of Augsburg Department of Software Engineering and Programming Languages Augsburg Germany

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Preface

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2020年 12071 LNCS卷 vi页

作者： Méry, Dominique Raschke, Alexander Houdek, Frank Institute of Software Engineering and Programming Languages Ulm University Ulm Germany LORIA Campus Scientifique Université de Lorraine Vandoeuvre-les-Nancy France Research and Development Mercedes-Benz AG Sindelfingen Germany

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SusTrainable: Promoting Sustainability as a Fundamental Driver in software Development Training and Education Teacher Training November 1–5, Nijmegen, The Netherlands Revised lecture notes

arXiv

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arXiv 2022年

作者： Koopman, Pieter Lubbers, Mart Fernandes, João Paulo Korečko, Štefan Paquete, Luís Porkoláb, Zoltán Szalay, Richárd Mauša, Goran Oprescu, Ana Koedijk, Lukas van Oostveen, Sander Kok, Stephan Li, Jianhao Zsók, Viktória Grbac, Tihana Galinac Grbac, Neven Valkanov, Vladimir Petrov, Mihail Radboud University Nijmegen Netherlands University of Porto Portugal Department of Computers and Informatics Faculty of Electrical Engineering and Informatics Technical University of Košice Slovakia CISUC Department of Informatics Engineering University of Coimbra Portugal Department of Programming Languages and Compilers Institute of Computer Science Faculty of Informatics Eötvös Lorád University Budapest Hungary University of Rijeka Faculty of Engineering Vukovarska 58 Rijeka51000 Croatia Complex Cyber Infrastructure University of Amsterdam Netherlands KPMG Software Engineering Master University of Amsterdam Netherlands Informatics Institute University of Amsterdam Netherlands Juraj Dobrila Univeristy of Pula Zagrebačka 30 PulaHR-52100 Croatia University Of Plovdiv "Paisii Hilendarski" Faculty of Mathematics and Informatics 24 Bulgaria Blvd. Plovdiv4027 Bulgaria

These are the proceedings of the first teacher training of the Erasmus+ project No. 2020-1-PT01-KA203-078646 -- Sustrainable. The full title of this project is Promoting Sustainability as a Fundamental Driver in software Development Training and Education and the interested reader may know more about it at: https://*** The flagship contribution is the organization of two summer schools on sustainable software production. The first summer school is moved due to the Corona pandemic. It is now scheduled for July 2022 in Rijeka, Croatia. This summer school will consist of lectures and practical work for master and PhD students in the area of computing science and closely related fields. There will be contributions from Plovdiv University, University of Ko\v{s}ice, University of Coimbra, University of Minho, E\"otv\"os Lor\'and University, University of Rijeka, Radboud University Nijmegen, University of Pula, University of Amsterdam and Babe\c{s}-Bolyai University. To prepare and streamline this summer school, the consortium organized a teacher training in Nijmegen, the Netherlands. This was an event of five full days on November 1-5, 2022 organized by Ingrid Berenbroek, Mart Lubbers and Pieter Koopman on the campus of the Radboud University Nijmegen, The Netherlands. Sustainability is one of the themes within the strategy of the Radboud University called `A Significant Impact', see https://***/sustainability/organisation/radboud-sustainable/. Sustainability plays an important role in education, research, and management. The contributions were reviewed and give a good overview of what can be expected from the summer school. Based on these papers and the very positive and constructive atmosphere, we expect a high quality and pleasant summer school. We are looking forward to contributing to a sustainable future. © 2022, CC BY-NC-ND.

关键词： Personnel training

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CLEER - AN AI-SYSTEM DEVELOPED TO ASSIST EQUIPMENT ARRANGEMENTS ON WARSHIPS

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NAVAL ENGINEERS JOURNAL 1989年第3期101卷 127-137页

作者： ZHOU, HH SILVERMAN, BG SIMKOL, J Dr. H. Harry Zhou:is a research professor at the Institute for Artificial Intelligence of The George Washington University. Dr. Zhou received his master's degree and Ph.D. in computer science from Vanderbilt University in 1984 and 1987 respectively. He did his dissertation in the fields of artificial intelligence analogical reasoning and machine learning. His research interests include: classifier systems genetic algorithms learning by analogy inductive learning adaptive expert systems automated knowledge acquisition and adaption. He is also interested in data base design programming languages mental modeling and software engineering. Dr. Barry G. Silverman:is director of the Institute for Artificial Intelligence and a professor at the Engineering Administration Department of The George Washington University. He is also president of IntelliTek Inc. an AI consulting firm. Dr. Silverman received the B.S.E. M.S.E. and Ph.D. degrees from the University of Pennsylvania. He has been a principal developer of four generic AI products as well as eight AI applications. Since 1979 he has written over 100 papers and reports on these AI efforts. Joel Simkol:is a research scientist currently engaged in designing expert system architectures to support electronic warfare vulnerability analyses threat assessments shipboard topside antenna arrangements and C3countermeasures employment. Mr. Simkol's work in applying expert systems technology to electromagnetic interference and to spectrum management has generated increased interest and participation from all branches of government agencies.

This paper describes a modularized AI system being built to help improve electromagnetic compatibility (EMC) among shipboard topside equipment and their associated systems. CLEER is intended to act as an easy to use integrator of existing expert knowledge and pre-existing data bases and large scale analytical models. Due to these interfaces; to the need for portability of the software; and to artificial intelligence related design requirements (such as the need for spatial reasoning, expert data base management, model base management, track-based reasoning, and analogical (similar ship) reasoning) it was realized that traditional expert system shells would be inappropriate, although relatively off-the-shelf AI technology could be incorporated. In the same vein, the rapid prototyping approach to expert system design and knowledge engineering was not pursued in favor of a rigorous systems engineering methodology. The critical design decisions affecting CLEER's development are summarized in this paper along with lessons learned to date all in terms of “how,” “why,” and “when” specific features are being developed.

关键词： Artificial Intelligence

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