Model Based Reasoning In Science And Engineering

Author: L. Magnani
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ISBN: 9781904987239
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The study of creative, diagnostic, visual, spatial, analogical, and temporal reasoning has demonstrated that there are many ways of performing intelligent and creative reasoning that cannot be described with the help only of traditional notions of reasoning such as classical logic. Understanding the contribution of modeling practices to discovery and conceptual change in science requires expanding scientific reasoning to include complex forms of creative reasoning that are not always successful and can lead to incorrect solutions. The study of these heuristic ways of reasoning is situated at the crossroads of philosophy, artificial intelligence, cognitive psychology, and logic; that is, at the heart of cognitive science. There are several key ingredients common to the various forms of model-based reasoning. The term "model" comprises both internal and external representations. The models are intended as interpretations of target physical systems, processes, phenomena, or situations. The models are retrieved or constructed on the basis of potentially satisfying salient constraints of the target domain. Moreover, in the modeling process, various forms of abstraction are used. Evaluation and adaptation take place in light of structural, causal, and/or functional constraints. Model simulation can be used to produce new states and enable evaluation of behaviors and other factors. Several of the papers in this volume aim at increasing epistemological knowledge about the role of model-based reasoning in various scientific tasks, other papers address fundamental cognitive issues related to model-based reasoning and illustrate novel analyses of cognitive "logical" models of model-based reasoning and of the interplay abduction/model-based reasoning/creative inferences. The volume is based on the papers that were presented at the International Conference Model-Based Reasoning in Science and Engineering: Abduction, Visualization, Simulation (MBR'04), held at the Collegio Ghislieri, University of Pavia, Pavia, Italy, in December 2004.

Model Based Reasoning

Author: Lorenzo Magnani
Editor: Springer Science & Business Media
ISBN: 9780306472442
File Size: 32,71 MB
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There are several key ingredients common to the various forms of model-based reasoning considered in this book. The term ‘model’ comprises both internal and external representations. The models are intended as interpretations of target physical systems, processes, phenomena, or situations and are retrieved or constructed on the basis of potentially satisfying salient constraints of the target domain. The book’s contributors are researchers active in the area of creative reasoning in science and technology.

Model Based Reasoning In Science Technology And Medicine

Author: Lorenzo Magnani
Editor: Springer
ISBN: 3540719865
File Size: 57,27 MB
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The volume is based on papers presented at the international conference on Model-Based Reasoning in Science and Medicine held in China in 2006. The presentations explore how scientific thinking uses models and explanatory reasoning to produce creative changes in theories and concepts. The contributions to the book are written by researchers active in the area of creative reasoning in science and technology. They include the subject area’s most recent results and achievements.

Model Based Reasoning In Scientific Discovery

Author: L. Magnani
Editor: Springer Science & Business Media
ISBN: 9780306462924
File Size: 24,12 MB
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The volume is based on the papers that were presented at the Interna tional Conference Model-Based Reasoning in Scientific Discovery (MBR'98), held at the Collegio Ghislieri, University of Pavia, Pavia, Italy, in December 1998. The papers explore how scientific thinking uses models and explanatory reasoning to produce creative changes in theories and concepts. The study of diagnostic, visual, spatial, analogical, and temporal rea soning has demonstrated that there are many ways of performing intelligent and creative reasoning that cannot be described with the help only of tradi tional notions of reasoning such as classical logic. Traditional accounts of scientific reasoning have restricted the notion of reasoning primarily to de ductive and inductive arguments. Understanding the contribution of model ing practices to discovery and conceptual change in science requires ex panding scientific reasoning to include complex forms of creative reasoning that are not always successful and can lead to incorrect solutions. The study of these heuristic ways of reasoning is situated at the crossroads of philoso phy, artificial intelligence, cognitive psychology, and logic; that is, at the heart of cognitive science. There are several key ingredients common to the various forms of model based reasoning to be considered in this book. The models are intended as in terpretations of target physical systems, processes, phenomena, or situations. The models are retrieved or constructed on the basis of potentially satisfying salient constraints of the target domain.

Model Based Reasoning In Science And Technology

Author: Lorenzo Magnani
Editor: Springer Science & Business Media
ISBN: 3642152228
File Size: 41,58 MB
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Systematically presented to enhance the feasibility of fuzzy models, this book introduces the novel concept of a fuzzy network whose nodes are rule bases and their interconnections are interactions between rule bases in the form of outputs fed as inputs.

Model Based Reasoning In Science And Technology

Author: Ángel Nepomuceno-Fernández
Editor: Springer Nature
ISBN: 3030327221
File Size: 47,93 MB
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This book discusses how scientific and other types of cognition make use of models, abduction, and explanatory reasoning in order to produce important and innovative changes in theories and concepts. Gathering revised contributions presented at the international conference on Model-Based Reasoning (MBR18), held on October 24–26 2018 in Seville, Spain, the book is divided into three main parts. The first focuses on models, reasoning, and representation. It highlights key theoretical concepts from an applied perspective, and addresses issues concerning information visualization, experimental methods, and design. The second part goes a step further, examining abduction, problem solving, and reasoning. The respective papers assess different types of reasoning, and discuss various concepts of inference and creativity and their relationship with experimental data. In turn, the third part reports on a number of epistemological and technological issues. By analyzing possible contradictions in modern research and describing representative case studies, this part is intended to foster new discussions and stimulate new ideas. All in all, the book provides researchers and graduate students in the fields of applied philosophy, epistemology, cognitive science, and artificial intelligence alike with an authoritative snapshot of the latest theories and applications of model-based reasoning.

Computer Systems Science And Engineering

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Deep Learning In Introductory Physics

Author: Mark J. Lattery
Editor: IAP
ISBN: 1681236303
File Size: 10,78 MB
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Deep Learning in Introductory Physics: Exploratory Studies of Model?Based Reasoning is concerned with the broad question of how students learn physics in a model?centered classroom. The diverse, creative, and sometimes unexpected ways students construct models, and deal with intellectual conflict, provide valuable insights into student learning and cast a new vision for physics teaching. This book is the first publication in several years to thoroughly address the “coherence versus fragmentation” debate in science education, and the first to advance and explore the hypothesis that deep science learning is regressive and revolutionary. Deep Learning in Introductory Physics also contributes to a growing literature on the use of history and philosophy of science to confront difficult theoretical and practical issues in science teaching, and addresses current international concern over the state of science education and appropriate standards for science teaching and learning. The book is divided into three parts. Part I introduces the framework, agenda, and educational context of the book. An initial study of student modeling raises a number of questions about the nature and goals of physics education. Part II presents the results of four exploratory case studies. These studies reproduce the results of Part I with a more diverse sample of students; under new conditions (a public debate, peer discussions, and group interviews); and with new research prompts (model?building software, bridging tasks, and elicitation strategies). Part III significantly advances the emergent themes of Parts I and II through historical analysis and a review of physics education research. ENDORSEMENTS: "In Deep Learning in Introductory Physics, Lattery describes his extremely innovative course in which students' ideas about motion are elicited, evaluated with peers, and revised through experiment and discussion. The reader can see the students' deep engagement in constructive scientific modeling, while students deal with counterintuitive ideas about motion that challenged Galileo in many of the same ways. Lattery captures students engaging in scientific thinking skills, and building difficult conceptual understandings at the same time. This is the 'double outcome' that many science educators have been searching for. The case studies provide inspiring examples of innovative course design, student sensemaking and reasoning, and deep conceptual change." ~ John Clement, University of Massachusetts—Amherst, Scientific Reasoning Research Institute "Deep Learning in Introductory Physics is an extraordinary book and an important intellectual achievement in many senses. It offers new perspectives on science education that will be of interest to practitioners, to education researchers, as well as to philosophers and historians of science. Lattery combines insights into modelbased thinking with instructive examples from the history of science, such as Galileo’s struggles with understanding accelerated motion, to introduce new ways of teaching science. The book is based on firsthand experiences with innovative teaching methods, reporting student’s ideas and discussions about motion as an illustration of how modeling and modelbuilding can help understanding science. Its lively descriptions of these experiences and its concise presentations of insights backed by a rich literature on education, cognitive science, and the history and philosophy of science make it a great read for everybody interested in how models shape thinking processes." ~ Dr. Jürgen Renn, Director, Max Planck Institute for the History of Science

Artificial Intelligence Abstracts

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Knowledge Management

Author: Irma Becerra-Fernandez
Editor: Prentice Hall
ISBN:
File Size: 44,53 MB
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For graduate-level courses in Knowledge Management and Decision Support Systems, this text presents a multi perspective approach to knowledge management: it spans electrical engineering, artificial intelligence, information systems, and business. It aims to provide students with the right combination of theory, technology and solutions.

Contemporary Knowledge Engineering And Cognition

Author: Franz Schmalhofer
Editor: Springer Verlag
ISBN:
File Size: 18,25 MB
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"This book has its source in the question of whether any knowledge engineering tools can be applied or analyzed in cognition research and what insights and methods of cognitive science might be relevant for knowledge engineers. It presents the proceedings of a workshop organized by the Special Interest Groups Cognition and Knowledge Engineering of the German Society for Informatics, held in February 1992 in Kaiserslautern. The book is structured into three parts. The first part contrasts work in knowledge engineering with approaches from the side of the "soft sciences". The second part deals with case-based approaches in expert systems. Cognition research and the cognitive adequacy of expert systems are discussed in the third part. Contributions from Canada, England, France, Switzerland, and the USA demonstrate how knowledge engineering and cognitive science are woven together internationally."--PUBLISHER'S WEBSITE.

Methodology For Design Analysis Of Reconfigurable Reusable Automotive Assembly Systems

Author: Zhenyu Kong
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File Size: 22,20 MB
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Current Literature On Science Of Science

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File Size: 64,25 MB
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Computer Aided Design And Computer Graphics

Author: Ji Zhou
Editor: SPIE-International Society for Optical Engineering
ISBN:
File Size: 43,83 MB
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The British National Bibliography

Author: Arthur James Wells
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ISBN:
File Size: 42,50 MB
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Kuwait Journal Of Science Engineering

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Reconceptualizing Stem Education

Author: Richard A. Duschl
Editor: Routledge
ISBN: 1317458508
File Size: 35,32 MB
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Reconceptualizing STEM Education explores and maps out research and development ideas and issues around five central practice themes: Systems Thinking; Model-Based Reasoning; Quantitative Reasoning; Equity, Epistemic, and Ethical Outcomes; and STEM Communication and Outreach. These themes are aligned with the comprehensive agenda for the reform of science and engineering education set out by the 2015 PISA Framework, the US Next Generation Science Standards and the US National Research Council’s A Framework for K-12 Science Education. The new practice-focused agenda has implications for the redesign of preK-12 education for alignment of curriculum-instruction-assessment; STEM teacher education and professional development; postsecondary, further, and graduate studies; and out-of-school informal education. In each section, experts set out powerful ideas followed by two eminent discussant responses that both respond to and provoke additional ideas from the lead papers. In the associated website highly distinguished, nationally recognized STEM education scholars and policymakers engage in deep conversations and considerations addressing core practices that guide STEM education.