This book is the third volume in a series that provides a hands-on perspective on the evolving theories associated with Roger Schank and his students. The primary focus of this volume is on constructing explanations. All of the chapters relate to the problem of building computer programs that can develop hypotheses about what might have caused an observed event. Because most researchers in natural language processing don't really want to work on inference, memory, and learning issues, most of their sample text fragments are chosen carefully to de-emphasize the need for non text-related reasoning. The ability to come up with hypotheses about what is really going on in a story is a hallmark of human intelligence. The biggest difference between truly intelligent readers and less intelligent ones is the extent to which the reader can go beyond merely understanding the explicit statements being communicated. Achieving a creative level of understanding means developing hypotheses about questions for which there may be no conclusively correct answer at all. The focus of the lab, during the period documented in this book, was to work on getting a computer program to do that. The volume adopts a case-based approach to the construction of explanations which suggests that the main steps in the process of explaining a given anomaly are as follows: * Retrieve an explanation that might be relevant to the anomaly. * Evaluate whether the retrieved explanation makes sense when applied to the current anomaly. * Adapt the explanation to produce a new variant that fits better if the retrieved explanation doesn't fit the anomaly perfectly.

This book provides an overview of computer techniques and tools — especially from artificial intelligence (AI) — for handling legal evidence, police intelligence, crime analysis or detection, and forensic testing, with a sustained discussion of methods for the modelling of reasoning and forming an opinion about the evidence, methods for the modelling of argumentation, and computational approaches to dealing with legal, or any, narratives. By the 2000s, the modelling of reasoning on legal evidence has emerged as a significant area within the well-established field of AI & Law. An overview such as this one has never been attempted before. It offers a panoramic view of topics, techniques and tools. It is more than a survey, as topic after topic, the reader can get a closer view of approaches and techniques. One aim is to introduce practitioners of AI to the modelling legal evidence. Another aim is to introduce legal professionals, as well as the more technically oriented among law enforcement professionals, or researchers in police science, to information technology resources from which their own respective field stands to benefit. Computer scientists must not blunder into design choices resulting in tools objectionable for legal professionals, so it is important to be aware of ongoing controversies. A survey is provided of argumentation tools or methods for reasoning about the evidence. Another class of tools considered here is intended to assist in organisational aspects of managing of the evidence. Moreover, tools appropriate for crime detection, intelligence, and investigation include tools based on link analysis and data mining. Concepts and techniques are introduced, along with case studies. So are areas in the forensic sciences. Special chapters are devoted to VIRTOPSY (a procedure for legal medicine) and FLINTS (a tool for the police). This is both an introductory book (possibly a textbook), and a reference for specialists from various quarters.

The Scandinavian Conference on Artificial Intelligence continues a tradition of being one of the most important regional AI conferences in Europe for ten years now. The topics of this year’s contributions have a broad range, from machine learning, knowledge representation, robotics, planning and scheduling, natural language, computer vision, search algorithms, industrial applications, to philosophical foundations. These contributions exemplify the diversity of research in artificial intelligence today and confirm the achievement and magnitude of 25 years AI research in Scandinavia. In this tenth edition there will be an overview of the past, present and future of artificial intelligence. Furthermore, attention will be paid to the industrial aspects of artificial intelligence and the impressions from Swedish AI through the years. Other topics discussed are biosurveillance and an elaboration on probalistic modelling and learning in a relational world.

This book seeks to narrow two gaps: first, between the widespread use of case studies and their frequently 'loose' methodological moorings; and second, between the scholarly community advancing methodological frontiers in case study research and the users of case studies in development policy and practice. It draws on the contributors' collective experience at this nexus, but the underlying issues are more broadly relevant to case study researchers and practitioners in all fields. How does one prepare a rigorous case study? When can causal inferences reasonably be drawn from a single case? When and how can policy-makers reasonably presume that a demonstrably successful intervention in one context might generate similarly impressive outcomes elsewhere, or if massively 'scaled up'? No matter their different starting points – disciplinary base, epistemological orientation, sectoral specialization, or practical concerns – readers will find issues of significance for their own field, and others across the social sciences. This title is also available Open Access.

Case Study Research in Practice explores the theory and practice of case study research. Helen Simons draws on her extensive experience of teaching and conducting case study to provide a comprehensive and practical account of how to design, conduct and communicate case study research. It addresses questions often raised by students and common misconceptions about case research. In four sections the book covers - Rationale, concept and design of case study research - Methods, ethics and reflexivity in case study - Interpreting, analyzing and reporting the case - Generalizing and theorizing in case study research Rich with 'tales from the field' and summary memos as an aide-memoire to future action, the book provides fresh insights and challenges for researchers to guide their practice of case study research. This is an ideal text for those studying and conducting case study research in education, health and social care, and related social science disciplines. Helen Simons is Professor Emeritus of Education University of Southampton

This work, a paradigm for modelling decision-making under uncertainty, describes the general theory and its relationship to planning, repeated choice problems, inductive inference, and learning; and highlights its mathematical and philosophical foundations.

This book constitutes the refereed proceedings of the 17th Conference on Artificial Intelligence in Medicine, AIME 2019, held in Poznan, Poland, in June 2019. The 22 revised full and 31 short papers presented were carefully reviewed and selected from 134 submissions. The papers are organized in the following topical sections: deep learning; simulation; knowledge representation; probabilistic models; behavior monitoring; clustering, natural language processing, and decision support; feature selection; image processing; general machine learning; and unsupervised learning.

This text demonstrates how various soft computing tools can be applied to design and develop methodologies and systems with case based reasoning, that is, for real-life decision-making or recognition problems. Comprising contributions from experts, it introduces the basic concepts and theories, and includes many reports on real-life applications. This book is of interest to graduate students and researchers in computer science, electrical engineering and information technology, as well as researchers and practitioners from the fields of systems design, pattern recognition and data mining.

The use of case studies to build and test theories in political science and the other social sciences has increased in recent years. Many scholars have argued that the social sciences rely too heavily on quantitative research and formal models and have attempted to develop and refine rigorous methods for using case studies. This text presents a comprehensive analysis of research methods using case studies and examines the place of case studies in social science methodology. It argues that case studies, statistical methods, and formal models are complementary rather than competitive. The book explains how to design case study research that will produce results useful to policymakers and emphasizes the importance of developing policy-relevant theories. It offers three major contributions to case study methodology: an emphasis on the importance of within-case analysis, a detailed discussion of process tracing, and development of the concept of typological theories. Case Studies and Theory Development in the Social Sciences will be particularly useful to graduate students and scholars in social science methodology and the philosophy of science, as well as to those designing new research projects, and will contribute greatly to the broader debate about scientific methods.