Download or read book Experimentation in Machine Discovery written by Deepak Kulkarni and published by . This book was released on 1990 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Machine Discovery written by Jan Zytkow and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 229 pages. Available in PDF, EPUB and Kindle. Book excerpt: Human and machine discovery are gradual problem-solving processes of searching large problem spaces for incompletely defined goal objects. Research on problem solving has usually focused on searching an `instance space' (empirical exploration) and a `hypothesis space' (generation of theories). In scientific discovery, searching must often extend to other spaces as well: spaces of possible problems, of new or improved scientific instruments, of new problem representations, of new concepts, and others. This book focuses especially on the processes for finding new problem representations and new concepts, which are relatively new domains for research on discovery. Scientific discovery has usually been studied as an activity of individual investigators, but these individuals are positioned in a larger social structure of science, being linked by the `blackboard' of open publication (as well as by direct collaboration). Even while an investigator is working alone, the process is strongly influenced by knowledge and skills stored in memory as a result of previous social interactions. In this sense, all research on discovery, including the investigations on individual processes discussed in this book, is social psychology, or even sociology.
Download or read book Methods and Applications of Autonomous Experimentation written by Marcus Noack and published by CRC Press. This book was released on 2023-12-14 with total page 575 pages. Available in PDF, EPUB and Kindle. Book excerpt: Autonomous Experimentation is poised to revolutionize scientific experiments at advanced experimental facilities. Whereas previously, human experimenters were burdened with the laborious task of overseeing each measurement, recent advances in mathematics, machine learning and algorithms have alleviated this burden by enabling automated and intelligent decision-making, minimizing the need for human interference. Illustrating theoretical foundations and incorporating practitioners’ first-hand experiences, this book is a practical guide to successful Autonomous Experimentation. Despite the field’s growing potential, there exists numerous myths and misconceptions surrounding Autonomous Experimentation. Combining insights from theorists, machine-learning engineers and applied scientists, this book aims to lay the foundation for future research and widespread adoption within the scientific community. This book is particularly useful for members of the scientific community looking to improve their research methods but also contains additional insights for students and industry professionals interested in the future of the field.
Download or read book The Processes of Scientific Discovery the Strategy of Experimentation written by Carnegie-Mellon University. Computer Science Dept and published by . This book was released on 1986 with total page 36 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper is part of program of research aimed at understanding the processes of scientific discovery by constructing computer programs that are capable of making discoveries and the simulate, at a grosser or finer level of approximation, the paths that have been followed by distinguished scientists on their roads to important discoveries. The present investigation is made possible by the existence of detailed historical study of a particular scientific discovery: Hans Krebs' elucidation of the chemical paths for synthesis or urea in the liver (Holmes, 1979). The system, Kekada, which have built does not, of course, capture the full detail of the actual historical process; but it does represent a serious attempt to describe both the knowledge and the heuristics that Krebs used in his research. Keywords: Machine learning: Scientific discovery; Cognitive psychology. (KR).
Download or read book Discovery Science written by Bernahrd Pfahringer and published by Springer Science & Business Media. This book was released on 2010-09-27 with total page 396 pages. Available in PDF, EPUB and Kindle. Book excerpt: The LNAI series reports state-of-the-art results in artificial intelligence research, development, and education, at a high level and in both printed and electronic form. Enjoying tight cooperation with the R & D community, with numerous individuals, as well as with prestigious organizations and societies, LNAI has grown into the most comprehensive artificial intelligence research forum available. The scope of LNAI spans the whole range of artificial intelligence and intelligent information processing including interdisciplinary topics in a variety of application fields. The type of material published traditionally includes proceedings (published in time for the respective conference) post-proceedings (consisting of thoroughly revised final full papers) research monographs (which may be based on PhD work) More recently, several color-cover sublines have been added featuring, beyond a collection of papers, various added-value components; these sublines include tutorials (textbook-like monographs or collections of lectures given at advance courses) state-of-the-art surveys (offering complete and mediated coverage of a topic) hot topics (introducing emergent topics to the broader community) In parallel to the printed book, each new volume is published electronically in LNCS Online. Book jacket.
Download or read book Model Based Reasoning in Scientific Discovery written by L. Magnani and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 339 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Rough Sets Fuzzy Sets and Knowledge Discovery written by Wojciech P. Ziarko and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 486 pages. Available in PDF, EPUB and Kindle. Book excerpt: The objective of this book is two-fold. Firstly, it is aimed at bringing to gether key research articles concerned with methodologies for knowledge discovery in databases and their applications. Secondly, it also contains articles discussing fundamentals of rough sets and their relationship to fuzzy sets, machine learning, management of uncertainty and systems of logic for formal reasoning about knowledge. Applications of rough sets in different areas such as medicine, logic design, image processing and expert systems are also represented. The articles included in the book are based on selected papers presented at the International Workshop on Rough Sets and Knowledge Discovery held in Banff, Canada in 1993. The primary methodological approach emphasized in the book is the mathematical theory of rough sets, a relatively new branch of mathematics concerned with the modeling and analysis of classification problems with imprecise, uncertain, or incomplete information. The methods of the theory of rough sets have applications in many sub-areas of artificial intelligence including knowledge discovery, machine learning, formal reasoning in the presence of uncertainty, knowledge acquisition, and others. This spectrum of applications is reflected in this book where articles, although centered around knowledge discovery problems, touch a number of related issues. The book is intended to provide an important reference material for students, researchers, and developers working in the areas of knowledge discovery, machine learning, reasoning with uncertainty, adaptive expert systems, and pattern classification.
Download or read book Biological Pattern Discovery With R Machine Learning Approaches written by Zheng Rong Yang and published by World Scientific. This book was released on 2021-09-17 with total page 462 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides the research directions for new or junior researchers who are going to use machine learning approaches for biological pattern discovery. The book was written based on the research experience of the author's several research projects in collaboration with biologists worldwide. The chapters are organised to address individual biological pattern discovery problems. For each subject, the research methodologies and the machine learning algorithms which can be employed are introduced and compared. Importantly, each chapter was written with the aim to help the readers to transfer their knowledge in theory to practical implementation smoothly. Therefore, the R programming environment was used for each subject in the chapters. The author hopes that this book can inspire new or junior researchers' interest in biological pattern discovery using machine learning algorithms.
Download or read book Chemical Experiments Illustrating the Theory Practice and Application of the Science of Chemistry written by George William Francis and published by . This book was released on 1855 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Machine Aided Biological Discovery and Design written by Sachit Dinesh Saksena and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in biotechnology and the life sciences are primarily driven by biologists conducting rigorous experimentation. However, biology is often too complex - with intractable combinatorial search spaces and functional landscapes - to comprehensively explore, understand, and engineer via iterative biological experimentation. Next-generation sequencing technologies have made it possible to measure biology in high-throughput, giving observational insight into these complexities. Further, in recent years, it has become possible to both manipulate biological systems with fine-grained control and directly synthesize large libraries of DNA molecules with specified sequences, providing unprecedented ability to engineer biology. We explore the thesis that computational methods that are built with experimental considerations and trained on carefully selected high-throughput experimental data can drive advances in the life sciences by making accurate predictions that can then be used to iteratively generate hypotheses and design biological sequences for further experimental validation. To test our thesis about the value of computational methods we introduce and apply computational approaches for modeling cellular differentiation trajectories, identifying non-specific antibodies, and designing diverse libraries of biological sequences that reflect desired objectives. First, we introduce a generative machine learning model for inferring cellular developmental landscapes from cross-sectional sequencing of in vitro differentiation time-series. We validate this model with ground-truth experimental lineage tracing experiments, and we show its ability to conduct in silico simulations of cellular differentiation trajectories with perturbations. Next, we present a computational framework for using sequencing data from therapeutic discovery campaigns to identify nonspecific antibody therapeutics in large candidate pools. We show that this approach bypasses and outperforms costly combinatorial affinity selection experiments and allows the use of only single-target selection data to identify pairwise nonspecificity. Finally, we introduce an algorithm for the rational design of high diversity synthetic antibody libraries using machine learning models and stochastic optimization. We show how this can be used to develop large libraries optimized for targets or developability characteristics leading to more promising candidates from affinity selection.
Download or read book Materials Discovery and Design written by Turab Lookman and published by Springer. This book was released on 2018-09-22 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book addresses the current status, challenges and future directions of data-driven materials discovery and design. It presents the analysis and learning from data as a key theme in many science and cyber related applications. The challenging open questions as well as future directions in the application of data science to materials problems are sketched. Computational and experimental facilities today generate vast amounts of data at an unprecedented rate. The book gives guidance to discover new knowledge that enables materials innovation to address grand challenges in energy, environment and security, the clearer link needed between the data from these facilities and the theory and underlying science. The role of inference and optimization methods in distilling the data and constraining predictions using insights and results from theory is key to achieving the desired goals of real time analysis and feedback. Thus, the importance of this book lies in emphasizing that the full value of knowledge driven discovery using data can only be realized by integrating statistical and information sciences with materials science, which is increasingly dependent on high throughput and large scale computational and experimental data gathering efforts. This is especially the case as we enter a new era of big data in materials science with the planning of future experimental facilities such as the Linac Coherent Light Source at Stanford (LCLS-II), the European X-ray Free Electron Laser (EXFEL) and MaRIE (Matter Radiation in Extremes), the signature concept facility from Los Alamos National Laboratory. These facilities are expected to generate hundreds of terabytes to several petabytes of in situ spatially and temporally resolved data per sample. The questions that then arise include how we can learn from the data to accelerate the processing and analysis of reconstructed microstructure, rapidly map spatially resolved properties from high throughput data, devise diagnostics for pattern detection, and guide experiments towards desired targeted properties. The authors are an interdisciplinary group of leading experts who bring the excitement of the nascent and rapidly emerging field of materials informatics to the reader.
Download or read book Man machine System Experiments written by Henry McIlvaine Parsons and published by Johns Hopkins University Press. This book was released on 1972 with total page 660 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Computational Models of Scientific Discovery and Theory Formation written by Jeff Shrager and published by Morgan Kaufmann. This book was released on 1990 with total page 520 pages. Available in PDF, EPUB and Kindle. Book excerpt: This collection reports on recent advances in the study of scientific discovery and theory formation based on the computational techniques of artificial intelligence and cognitive science.
Download or read book Organic Computing A Paradigm Shift for Complex Systems written by Christian Müller-Schloer and published by Springer Science & Business Media. This book was released on 2011-04-29 with total page 629 pages. Available in PDF, EPUB and Kindle. Book excerpt: Organic Computing has emerged as a challenging vision for future information processing systems. Its basis is the insight that we will increasingly be surrounded by and depend on large collections of autonomous systems, which are equipped with sensors and actuators, aware of their environment, communicating freely, and organising themselves in order to perform actions and services required by the users. These networks of intelligent systems surrounding us open fascinating ap-plication areas and at the same time bear the problem of their controllability. Hence, we have to construct such systems as robust, safe, flexible, and trustworthy as possible. In particular, a strong orientation towards human needs as opposed to a pure implementation of the tech-nologically possible seems absolutely central. The technical systems, which can achieve these goals will have to exhibit life-like or "organic" properties. "Organic Computing Systems" adapt dynamically to their current environmental conditions. In order to cope with unexpected or undesired events they are self-organising, self-configuring, self-optimising, self-healing, self-protecting, self-explaining, and context-aware, while offering complementary interfaces for higher-level directives with respect to the desired behaviour. First steps towards adaptive and self-organising computer systems are being undertaken. Adaptivity, reconfigurability, emergence of new properties, and self-organisation are hot top-ics in a variety of research groups worldwide. This book summarises the results of a 6-year priority research program (SPP) of the German Research Foundation (DFG) addressing these fundamental challenges in the design of Organic Computing systems. It presents and discusses the theoretical foundations of Organic Computing, basic methods and tools, learning techniques used in this context, architectural patterns and many applications. The final outlook shows that in the mean-time Organic Computing ideas have spawned a variety of promising new projects.
Download or read book Information Science for Materials Discovery and Design written by Turab Lookman and published by Springer. This book was released on 2015-12-12 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book deals with an information-driven approach to plan materials discovery and design, iterative learning. The authors present contrasting but complementary approaches, such as those based on high throughput calculations, combinatorial experiments or data driven discovery, together with machine-learning methods. Similarly, statistical methods successfully applied in other fields, such as biosciences, are presented. The content spans from materials science to information science to reflect the cross-disciplinary nature of the field. A perspective is presented that offers a paradigm (codesign loop for materials design) to involve iteratively learning from experiments and calculations to develop materials with optimum properties. Such a loop requires the elements of incorporating domain materials knowledge, a database of descriptors (the genes), a surrogate or statistical model developed to predict a given property with uncertainties, performing adaptive experimental design to guide the next experiment or calculation and aspects of high throughput calculations as well as experiments. The book is about manufacturing with the aim to halving the time to discover and design new materials. Accelerating discovery relies on using large databases, computation, and mathematics in the material sciences in a manner similar to the way used to in the Human Genome Initiative. Novel approaches are therefore called to explore the enormous phase space presented by complex materials and processes. To achieve the desired performance gains, a predictive capability is needed to guide experiments and computations in the most fruitful directions by reducing not successful trials. Despite advances in computation and experimental techniques, generating vast arrays of data; without a clear way of linkage to models, the full value of data driven discovery cannot be realized. Hence, along with experimental, theoretical and computational materials science, we need to add a “fourth leg’’ to our toolkit to make the “Materials Genome'' a reality, the science of Materials Informatics.
Download or read book Chemoinformatics Approaches to Structure and Ligand Based Drug Design Volume II written by Adriano D. Andricopulo and published by Frontiers Media SA. This book was released on 2022-07-27 with total page 325 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book One Thousand Experiments in chemistry with illustrations of natural phenomena and practical observations on the manufacturing and chemical processes pursued in the useful arts A new edition improved with engravings etc written by Colin Mackenzie and published by . This book was released on 1823 with total page 752 pages. Available in PDF, EPUB and Kindle. Book excerpt:
