Abduction with hypotheses confirmation

2021

We present an abductive search strategy that integrates creative abduction and probabilistic reasoning to produce plausible explanations for unexplained observations. Using a graphical model representation of abductive search, we introduce a heuristic approach to hypothesis generation, comparison, and selection. To identify creative and plausible explanations, we propose 1) applying novel structural similarity metrics to a search for simple explanations, and 2) optimizing for the probability of a hypothesis’ occurrence given known observa-

THEORIA. An International Journal for Theory, History …, 2009

The purpose of this piece is to provide a critical analysis on some key aspects of abduction, as conceived by several researchers through my book Abductive Reasoning. These contributions raise fundamental questions concerning the conjectural character of ...

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AI Magazine, 2002

Broadly speaking, abduction is a reasoning process invoked to ex-plain a puzzling observation. There are however, a variety of differ-ent approaches that claim to capture the true nature of this concept. One reason for this diversity lies in the fact that abductive ...

We propose that the process of abduction is a useful tool for how management scholars can better develop new explanatory hypotheses and theories. In doing so, we differentiate abduction from the more commonly studied methods of deduction and induction. We briefly explain the various research streams on abductive reasoning and propose a version that is fo-cused more on the process of abductive reasoning and less on the outcomes. We argue that by using contrastive reasoning and by recognizing different triggers of abduction, this process can help guide researchers to the types of causal explanations that are interesting. We conclude with some examples of abduction in the history of management research and a discussion of features of the reasoning processes involved.

Journal of Logic and Computation, 2007

The paper provides a uniform representation of abductive reasoning in the logical framework of causal inference relations. The representation covers in a single framework not only traditional, 'classical' forms of abduction, but also abductive reasoning in diagnosis, theories of actions and change, and abductive logic programming.

New Generation Computing, 2003

Recently, researches on discovery science and knowledge discovery have been carried out in various fields. Basically they are types of learning that learn tendencies from the sets of data of the same or similar categories. In this sense, discovery is to discover the tendencies. As a result, they cannot predict the events that are different from the trend. On the other hand, abduction is thought of as an explanatory reasoning. Indeed, abduction is a reasoning to generate hypotheses to explain an observation. However, the original meaning of abduction was to discover new things that cannot be known in a simple way. In this paper, abduction is defined using the original definition that discovers something that cannot be easily predicted. Then, this paper shows a role of abduction that can suggest or foresee the events that are different from the trend. In fact, Abductive Analogical Reasoning that can generate new hypotheses is adopted to solve the problem.

1993

Many abductive understanding systems explain novel situations by a c haining process that is neutral to explainer needs beyond generating some plausible explanation for the event being explained. This paper examines the relationship of standard models of abductive understanding to the case-based explanation model. In case-based explanation, construction and selection of abductive h ypotheses are focused by speci c explanations of prior episodes and by goal-based criteria re ecting current information needs. The case-based method is inspired by observations of human explanation of anomalous events during everyday understanding, and this paper focuses on the method's contributions to the problems of building good explanations in everyday d o m a i n s. W e i d e n tify ve central issues, compare how those issues are addressed in traditional and case-based explanation models, and discuss motivations for using the case-based approach to facilitate generation of plausible and useful explanations in domains that are complex and imperfectly understood.

IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 1998

FLAP, 2016

This special issue of the IfColog Journal of Logics and their Applications "Frontiers of Abduction" is based on a selection of papers concerning abduction that are situated at the crossroad of logic, epistemology, and cognitive science. The status of abduction-that Hintikka considered the fundamental problem of contemporary epistemology [7]-is very controversial. When dealing with abductive reasoning, misinterpretations and equivocations are common. What are the differences between abduction and induction? What are the differences between abduction and the well-known hypothetico-deductive method? What did Peirce mean when he considered abduction a kind of inference? Does abduction involve only the generation of hypotheses or their evaluation too? Are the criteria for the best explanation in abductive reasoning epistemic, or pragmatic, or both? How many kinds of abduction are there? The papers presented in this issue aim at increasing knowledge-logical, cognitive, epistemological-about creative and expert inferences. Analyzing various reasoning and modeling practices provides a way of specifying the nature of some abductive reasoning processes. Following [12; 14] we have to distinguish between sentential, model-based, and manipulative abduction. In the past decades many attempts have been made to model abduction by developing some formal tools in order to illustrate its computational properties and the relationships with the different forms of deductive reasoning (see, for example, [4]). Some of these formal models of abductive reasoning are based on the theory of the epistemic state of an agent [3], where the epistemic state of an individual is modeled as a consistent set of beliefs that can change by expansion and contraction (belief revision framework). These formal tools, often related to the tradition of nonmonotonic logic, logic programming, semantic tableaux, dynamic and adaptive logic, etc., are usually devoted to illustrating sentential abduction and present some limitations [13]. It may be said that the traditional logical accounts of abduction certainly illustrate much of what is important in abductive reasoning, especially the objective of selecting a set of hypotheses (diagnoses, causes) that are able to dispense good (preferred) explanations of data (observations), but fail in accounting for many cases of explanations occurring in science or in everyday reasoning. The logical papers