This volume explores abductive cognition, an important but, at least until the third quarter of the last century, neglected topic in cognition. It aims at increasing knowledge about creative and expert inferences.

This book gathers together novel essays on the state-of-the-art research into the logic and practice of abduction. In many ways, abduction has become established and essential to several fields, such as logic, cognitive science, artificial intelligence, philosophy of science, and methodology. In recent years this interest in abduction’s many aspects and functions has accelerated. There are evidently several different interpretations and uses for abduction. Many fundamental questions on abduction remain open. How is abduction manifested in human cognition and intelligence? What kinds or types of abduction can be discerned? What is the role for abduction in inquiry and mathematical discovery? The chapters aim at providing answer to these and other current questions. Their contributors have been at the forefront of discussions on abduction, and offer here their updated approaches to the issues that they consider central to abduction’s contemporary relevance. The book is an essential reading for any scholar or professional keeping up with disciplines impacted by the study of abductive reasoning, and its novel development and applications in various fields.

This Handbook offers the first comprehensive reference guide to the interdisciplinary field of abductive cognition, providing readers with extensive information on the process of reasoning to hypotheses in humans, animals, and in computational machines. It highlights the role of abduction in both theory practice: in generating and testing hypotheses and explanatory functions for various purposes and as an educational device. It merges logical, cognitive, epistemological and philosophical perspectives with more practical needs relating to the application of abduction across various disciplines and practices, such as in diagnosis, creative reasoning, scientific discovery, diagrammatic and ignorance-based cognition, and adversarial strategies. It also discusses the inferential role of models in hypothetical reasoning, abduction and creativity, including the process of development, implementation and manipulation for different scientific and technological purposes. Written by a group of internationally renowned experts in philosophy, logic, general epistemology, mathematics, cognitive, and computer science, as well as life sciences, engineering, architecture, and economics, the Handbook of Abductive Cognition offers a unique reference guide for readers approaching the process of reasoning to hypotheses from different perspectives and for various theoretical and practical purposes. Numerous diagrams, schemes and other visual representations are included to promote a better understanding of the relevant concepts and to make concepts highly accessible to an audience of scholars and students with different scientific backgrounds.

This book ties together the concerns of philosophers of science and AI researchers, showing for example the connections between scientific thinking and medical expert systems. It lays out a useful general framework for discussion of a variety of kinds of abduction. It develops important ideas about aspects of abductive reasoning that have been relatively neglected in cognitive science, including the use of visual and temporal representations and the role of abduction in the withdrawal of hypotheses.

“Artificial intelligence has always inspired outlandish visions—that AI is going to destroy us, save us, or at the very least radically transform us. Erik Larson exposes the vast gap between the actual science underlying AI and the dramatic claims being made for it. This is a timely, important, and even essential book.” —John Horgan, author of The End of Science Many futurists insist that AI will soon achieve human levels of intelligence. From there, it will quickly eclipse the most gifted human mind. The Myth of Artificial Intelligence argues that such claims are just that: myths. We are not on the path to developing truly intelligent machines. We don’t even know where that path might be. Erik Larson charts a journey through the landscape of AI, from Alan Turing’s early work to today’s dominant models of machine learning. Since the beginning, AI researchers and enthusiasts have equated the reasoning approaches of AI with those of human intelligence. But this is a profound mistake. Even cutting-edge AI looks nothing like human intelligence. Modern AI is based on inductive reasoning: computers make statistical correlations to determine which answer is likely to be right, allowing software to, say, detect a particular face in an image. But human reasoning is entirely different. Humans do not correlate data sets; we make conjectures sensitive to context—the best guess, given our observations and what we already know about the world. We haven’t a clue how to program this kind of reasoning, known as abduction. Yet it is the heart of common sense. Larson argues that all this AI hype is bad science and bad for science. A culture of invention thrives on exploring unknowns, not overselling existing methods. Inductive AI will continue to improve at narrow tasks, but if we are to make real progress, we must abandon futuristic talk and learn to better appreciate the only true intelligence we know—our own.

This book is about abduction, 'the logic of Sherlock Holmes', and about how some kinds of abductive reasoning can be programmed in a computer. The work brings together Artificial Intelligence and philosophy of science and is rich with implications for other areas such as, psychology, medical informatics, and linguistics. It also has subtle implications for evidence evaluation in areas such as accident investigation, confirmation of scientific theories, law, diagnosis, and financial auditing. The book is about certainty and the logico-computational foundations of knowledge; it is about inference in perception, reasoning strategies, and building expert systems.

A novel defense of abduction, one of the main forms of nondeductive reasoning. With this book, Igor Douven offers the first comprehensive defense of abduction, a form of nondeductive reasoning. Abductive reasoning, which is guided by explanatory considerations, has been under normative pressure since the advent of Bayesian approaches to rationality. Douven argues that, although it deviates from Bayesian tenets, abduction is nonetheless rational. Drawing on scientific results, in particular those from reasoning research, and using computer simulations, Douven addresses the main critiques of abduction. He shows that versions of abduction can perform better than the currently popular Bayesian approaches—and can even do the sort of heavy lifting that philosophers have hoped it would do. Douven examines abduction in detail, comparing it to other modes of inference, explaining its historical roots, discussing various definitions of abduction given in the philosophical literature, and addressing the problem of underdetermination. He looks at reasoning research that investigates how judgments of explanation quality affect people’s beliefs and especially their changes of belief. He considers the two main objections to abduction, the dynamic Dutch book argument, and the inaccuracy-minimization argument, and then gives abduction a positive grounding, using agent-based models to show the superiority of abduction in some contexts. Finally, he puts abduction to work in a well-known underdetermination argument, the argument for skepticism regarding the external world.

This book employs a new eco-cognitive model of abduction to underline the distributed and embodied nature of scientific cognition. Its main focus is on the knowledge-enhancing virtues of abduction and on the productive role of scientific models. What are the distinctive features that define the kind of knowledge produced by science? To provide an answer to this question, the book first addresses the ideas of Aristotle, who stressed the essential inferential and distributed role of external cognitive tools and epistemic mediators in abductive cognition. This is analyzed in depth from both a naturalized logic and an ecology of cognition perspective. It is shown how the maximization of cognition, and of abducibility – two typical goals of science – are related to a number of fundamental aspects: the optimization of the eco-cognitive situatedness; the maximization of changeability for both the input and the output of the inferences involved; a high degree of information-sensitiveness; and the need to record the “past life” of abductive inferential practices. Lastly, the book explains how some impoverished epistemological niches – the result of a growing epistemic irresponsibility associated with the commodification and commercialization of science – are now seriously jeopardizing the flourishing development of human creative abduction.

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.