In informal terms, abductive reasoning involves inferring the best or most plausible explanation from a given set of facts or data. It is a common occurrence in everyday life and crops up in such diverse places as medical diagnosis, scientific theory formation, accident investigation, language understanding, and jury deliberation. In recent years, it has become a popular and fruitful topic in artificial intelligence research. This volume breaks new ground in the scientific, philosophical, and technological study of abduction. It presents new (...) ideas about inferential and information-processing foundations for knowledge and certainty. The authors argue that knowledge arises from experience by processes of abductive inference, in contrast to the view that it arises non-inferentially, or that deduction and inductive generalization are enough to account for knowledge. Much AI research is hypothetical, so the importance of this book is that it reports key discoveries about abduction that have been made as a result of designing, building, testing, and analyzing actual working knowledge-based systems for medical diagnosis and other abductive tasks. The book tells the story of six generations of increasingly sophisticated generic abduction machines, RED-1, RED-2, PEIRCE, MDX2, TIPS, QUAWDS, and the discovery of reasoning strategies that make it computationally feasible to form well-justified composite explanatory hypotheses, despite the threat of combinatorial explosion. The final chapter argues that perception is logically abductive and presents a layered-abduction computational model of perceptual information processing. This book will be of great interest to researchers in AI, cognitive science, and philosophy of science. (shrink)

We describe a general problem solving mechanism that is especially suited for performing a particular form of abductive inference, or best-explanation finding. A problem solver embodying this mechanism synthesizes composite hypotheses. It does so by by combining hypothesis parts as a means to the satisfaction of explanatory goals. In this way it is able to arrive at complex, integrated conclusions which are not pre-stored.The intent is to present a computationally-feasible, task-specific problem solver for a particular information processing task which is (...) nevertheless of very great generality. The task is that of synthesizing coherent composite explanatory hypotheses based upon a prestored, and possibly vast collection of hypothesis-generating “concepts”. The authors’ claim is nothing less than to have shown, in a new sense, and surpassing all other work in this area, how it is computationally possible for an agent to come to “know”, based upon the evidence of the case. (shrink)

Red-2 is a computer program for red-cell antibody identification, a piece of "normal science". Abstracting from Red-2, a general problem solving mechanism is described that is especially suited for performing a form of abductive inference or best explanation finding. A problem solver embodying this mechanism synthesizes composite hypotheses by combining hypothesis parts. This is a common task of intelligence, and a component of scientific reasoning. The work addresses the question, 'How is science possible?' by showing how a simple but powerful (...) form of hypothesis synthesis is computationally feasible. (shrink)

This paper describes in some detail a pattern of justification which seems to be part of common sense logic and also part of the logic of scientific investigations. Calling this pattern “abduction,” the paper lays out an “abduction-prediction” model of scientific inference as an update to the traditional hypothetico-deductive model. According to this newer model, scientific theories receive their claims for acceptance and belief from the abductive arguments that support them, and the processes of scientific discovery aim to develop theories (...) with strong abductive support. It is suggested that the study of diagnosis presents a good opportunity for studying abduction under somewhat simpler and more reproducible conditions than occur in scientific discovery. A computer-based diagnostic system is described which provides a small-scale validation of the abduc-tion-prediction model by showing that a version of it can be made precise enough to be implemented and to perform correctly for diagnosis. (shrink)