Information technology has been, in recent years, under increasing commercial pressure to provide devices and systems which help/ replace the human in his daily activity. This pressure requires the use of logic as the underlying foundational workhorse of the area. New logics were developed as the need arose and new foci and balance has evolved within logic itself. One aspect of these new trends in logic is the rising impor tance of model based reasoning. Logics have become more and more tailored to applications and their reasoning has become more and more application dependent. In fact, some years ago, I myself coined the phrase "direct deductive reasoning in application areas", advocating the methodology of model-based reasoning in the strongest possible terms. Certainly my discipline of Labelled Deductive Systems allows to bring "pieces" of the application areas as "labels" into the logic. I therefore heartily welcome this important book to Volume 25 of the Applied Logic Series and see it as an important contribution in our overall coverage of applied logic.

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.

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.

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.

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.

Advanced visual analysis and problem solving has been conducted successfully for millennia. The Pythagorean Theorem was proven using visual means more than 2000 years ago. In the 19th century, John Snow stopped a cholera epidemic in London by proposing that a specific water pump be shut down. He discovered that pump by visually correlating data on a city map. The goal of this book is to present the current trends in visual and spatial analysis for data mining, reasoning, problem solving and decision-making. This is the first book to focus on visual decision making and problem solving in general with specific applications in the geospatial domain - combining theory with real-world practice. The book is unique in its integration of modern symbolic and visual approaches to decision making and problem solving. As such, it ties together much of the monograph and textbook literature in these emerging areas. This book contains 21 chapters that have been grouped into five parts: (1) visual problem solving and decision making, (2) visual and heterogeneous reasoning, (3) visual correlation, (4) visual and spatial data mining, and (5) visual and spatial problem solving in geospatial domains. Each chapter ends with a summary and exercises. The book is intended for professionals and graduate students in computer science, applied mathematics, imaging science and Geospatial Information Systems (GIS). In addition to being a state-of-the-art research compilation, this book can be used a text for advanced courses on the subjects such as modeling, computer graphics, visualization, image processing, data mining, GIS, and algorithm analysis.

This book presents adaptive logics as an intuitive and powerful framework for modeling defeasible reasoning. It examines various contexts in which defeasible reasoning is useful and offers a compact introduction into adaptive logics. The author first familiarizes readers with defeasible reasoning, the adaptive logics framework, combinations of adaptive logics, and a range of useful meta-theoretic properties. He then offers a systematic study of adaptive logics based on various applications. The book presents formal models for defeasible reasoning stemming from different contexts, such as default reasoning, argumentation, and normative reasoning. It highlights various meta-theoretic advantages of adaptive logics over other logics or logical frameworks that model defeasible reasoning. In this way the book substantiates the status of adaptive logics as a generic formal framework for defeasible reasoning.

Scientists use concepts and principles that are partly specific for their subject matter, but they also share part of them with colleagues working in different fields. Compare the biological notion of a 'natural kind' with the general notion of 'confirmation' of a hypothesis by certain evidence. Or compare the physical principle of the 'conservation of energy' and the general principle of 'the unity of science'. Scientists agree that all such notions and principles aren't as crystal clear as one might wish. An important task of the philosophy of the special sciences, such as philosophy of physics, of biology and of economics, to mention only a few of the many flourishing examples, is the clarification of such subject specific concepts and principles. Similarly, an important task of 'general' philosophy of science is the clarification of concepts like 'confirmation' and principles like 'the unity of science'. It is evident that clarfication of concepts and principles only makes sense if one tries to do justice, as much as possible, to the actual use of these notions by scientists, without however following this use slavishly. That is, occasionally a philosopher may have good reasons for suggesting to scientists that they should deviate from a standard use. Frequently, this amounts to a plea for differentiation in order to stop debates at cross-purposes due to the conflation of different meanings. While the special volumes of the series of Handbooks of the Philosophy of Science address topics relative to a specific discipline, this general volume deals with focal issues of a general nature. After an editorial introduction about the dominant method of clarifying concepts and principles in philosophy of science, called explication, the first five chapters deal with the following subjects. Laws, theories, and research programs as units of empirical knowledge (Theo Kuipers), various past and contemporary perspectives on explanation (Stathis Psillos), the evaluation of theories in terms of their virtues (Ilkka Niiniluto), and the role of experiments in the natural sciences, notably physics and biology (Allan Franklin), and their role in the social sciences, notably economics (Wenceslao Gonzalez). In the subsequent three chapters there is even more attention to various positions and methods that philosophers of science and scientists may favor: ontological, epistemological, and methodological positions (James Ladyman), reduction, integration, and the unity of science as aims in the sciences and the humanities (William Bechtel and Andrew Hamilton), and logical, historical and computational approaches to the philosophy of science (Atocha Aliseda and Donald Gillies).The volume concludes with the much debated question of demarcating science from nonscience (Martin Mahner) and the rich European-American history of the philosophy of science in the 20th century (Friedrich Stadler). - Comprehensive coverage of the philosophy of science written by leading philosophers in this field - Clear style of writing for an interdisciplinary audience - No specific pre-knowledge required

The book analyses the concept of discoverability, and some current epistemological problems related to it, with a special attention to science. It shows that discoverability is closely related to the sustainability of human creativity in an "eco-cognitive" perspective. Advocating the need of an integral ecology and leveraging the important concept of abduction, it demonstrates that an ecology of human creativity should have priority over other needs, i.e that the first ecological duty is to protect and sustain discoverability. Enhancing discoverability will protect human creativity, and it is exactly human creativity, a form of innovative abductive cognition, that can promote the implementation of the other kinds of ecology. The author guides readers through a comprehensive discussion on the concept of discoverability, eco-cognitive situatedness, and eco-cognitive openness and closure alike. By describing some key real-world examples, he highlights the main challenges that are currently posed to human creativity and epistemic integrity. He also describes future eco-cognitive settings, discussing the problem of overcomputationalism and suggesting a reinterpretation of the role of human knowledge. Overall, this book fills an important gap in the literature on the nexus abduction – creativity – discovery, offering a source of inspiration to philosophers, epistemologists, and cognitive scientists. Yet, it also addresses researchers in other disciplines interested in the problems of scientific discovery and epistemic integrity of research.

This book discusses how scientific and other types of cognition make use of models, abduction, and explanatory reasoning in order to produce important or creative changes in theories and concepts. It includes revised contributions presented during the international conference on Model-Based Reasoning (MBR’015), held on June 25-27 in Sestri Levante, Italy. The book is divided into three main parts, the first of which focuses on models, reasoning and representation. It highlights key theoretical concepts from an applied perspective, addressing issues concerning information visualization, experimental methods and design. The second part goes a step further, examining abduction, problem solving and reasoning. The respective contributions analyze different types of reasoning, discussing various concepts of inference and creativity and their relationship with experimental data. In turn, the third part reports on a number of historical, epistemological and technological issues. By analyzing possible contradictions in modern research and describing representative case studies in experimental research, this part aims at fostering new discussions and stimulating new ideas. All in all, the book provides researchers and graduate students in the field of applied philosophy, epistemology, cognitive science and artificial intelligence alike with an authoritative snapshot of current theories and applications of model-based reasoning.