Abduction, reason, and science: processes of discovery and explanation-a review

his discovery does not work. His heliocentric view allowed him to think that the sun, so near to the center of the planetary system, and so large, must somehow cause the planets to move as they do. In addition to this strong conjecture, he also had to generalize his findings for Mars to all planets by assuming that the same physical conditions could be obtained throughout the solar system. Research on abduction in AI dates back to the 1970s, but it is only fairly Broadly speaking, abduction is a reasoning process invoked to explain a puzzling observation. There are however, a variety of different approaches that claim to capture the true nature of this concept. One reason for this diversity lies in the fact that abductive reasoning occurs in a multitude of contexts. It concerns cases that cover the simplest selection of already existing hypotheses to the generation of new concepts in science. It also concerns cases where the observation is puzzling because it is novel versus cases in which the surprise concerns an anomalous observation. For example, if we wake up, and the lawn is wet, we might explain this observation by assuming that it must have rained or that the sprinklers have been on. This is a practical setting found in our day-today commonsense reasoning when a novel phenomenon is needed for an explanation. Abduction also occurs in more theoretical scientific contexts. For example, it has been claimed (Hanson 1961; Peirce 1958) that Johannes Kepler’s great discovery that the orbit of the planets is elliptical rather than circular was a prime piece of abductive reasoning. What initially led to this discovery was his anomalous observation that the longitudes of Mars did not fit circular orbits. Moreover, before even dreaming that the best explanation involved ellipses instead of circles, he tried several other forms. Kepler had to make some other assumptions about the planetary system, without which been conceptually challenging but also shows a (terminological) confusion with its close neighbor, induction. This book contributes to the rapidly growing literature on the topic of abductive reasoning. By placing abduction at the heart of the foundations of AI from philosophical, cognitive, and computational perspectives, the author makes us aware that abduction is not at all a new topic of research. In addition, by introducing fine distinctions in abductive kinds, it shows its relevance as a recent topic of research in all these fields. The importance of abduction has been recognized by leading researchers in all relevant fields. Although for Jaakko Hintikka, abduction is the fundamental problem of contemporary epistemology, in which abductive inferences are assembled as “answers to the inquirer’s explicit or (usually) tacit question put to some definite source of answers (information)” (p. 129), for Herbert Simon, the nature of the retroductive process (another term for abduction) “is the main subject of the theory of problem solving” (p. 16). For Paul Thagard, several kinds of abduction play a key role as heuristic strategies in the program PI (for “processes of induction”), a working system devoted to explaining in computational terms the main problems of philosophy of science, such as scientific discovery, explanation, and evaluation (p. 49). In agreement with other current approaches to abduction, for the author of this book, there are two main epistemological meanings of the word abduction: (1) abduction that only generates plausible hypotheses and (2) abduction as inference to the best explanation that also evaluates them to further obtain the best one. In this book, the first meaning is further divided into selective or creative. Selection takes place in contexts such as medical diagnosis, in which the task is to select a diagnosis from a precompiled set of diagnostic entities. Creativity is present in issues such as the discovery of a new disease. The latter meaning, abduction as inference to the best explanation, is described by the complete abductiondeduction-induction cycle, represented in an epistemological model for diagrecently that it has attracted great interest in areas such as logic programming, knowledge assimilation, and diagnosis. It has been a topic of several workshops at AI conferences (1996, 1998, 2000 European Conference on Artificial Intelligence; 1997 International Joint Conference on Artificial Intelligence) as well as model-based reasoning conferences (1998, 2001 Model-Based Reasoning Conference). It has also been at the center of recent publications (Flach and Kakas 2000; Josephson and Josephson 1994). In all these places, the discussion about the different aspects of abduction has Abduction, Reason, and Science: Processes of Discovery and Explanation, Lorenzo Magnani, New York, Kluwer Academic/Plenum Publishers, 2001, 205 pages, ISBN 0306-46514-0 (hardback).