In informal terms, abductive reasoning involves inferring the best or most plausible explanation from a given set of facts or data. This volume 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 noninferentially, or that deduction and inductive generalization are enough to account for knowledge. The book tells the story of six generations of increasingly sophisticated generic abduction machines and the discovery of reasoning strategies that make it computationally feasible to form well-justified composite explanatory hypotheses, despite the threat of combinatorial explosion. This book will be of great interest to researchers in AI, cognitive science, and philosophy of science.
Introduction; 1. Conceptual analysis of abduction: what is abduction?; 2. Knowledge-based systems and the science of AI: 3. Two RED systems; 4. Generalizing the control strategy; 5. More kinds of knowledge: TIPS and PATHEX/LIVER TIPS; 6. Better task analysis, better strategy; 7. Computational complexity of abduction; 8. Diagnostic systems MDX2 and QUADS; 9. Practical abduction; 10. Perception and language understanding; Appendices.