Abductive reasoning

About: Abductive reasoning is a research topic. Over the lifetime, 1917 publications have been published within this topic receiving 44645 citations. The topic is also known as: abduction & abductive inference.

Reasoning from Evidence: Inductive Logic

Abstract: A rotationally symmetric component and method of producing it, for optical imaging systems having a quasi-aspherical concave surface obtained as a result of cementing to a base lens a second lens, then grinding the combined lenses so that only an annular rim lens is left of the second lens, and optionally cementing thereto a third lens, and/or a fourth lens, while each time grinding and polishing the composite body to reduce the additional lenses to rim lenses, the radii and optical characteristics being of such a magnitude that the combined refractive characteristics resemble those of an aspherical surface.

Abductive inference in bayesian networks: finding a partition of the explanation space

Abstract: This paper proposes a new approach to the problem of ob- taining the most probable explanations given a set of observations in a Bayesian network. The method provides a set of possibilities ordered by their probabilities. The main novelties are that the level of detail of each one of the explanations is not uniform (with the idea of being as simple as possible in each case), the explanations are mutually exclusive, and the number of required explanations is not fixed (it depends on the particular case we are solving). Our goals are achieved by means of the construction of the so called explanation tree which can have asym- metric branching and that will determine the different possibilities. This paper describes the procedure for its computation based on information theoretic criteria and shows its behaviour in some simple examples.

How Analysts Think (?): Early Observations

Abstract: In this paper we describe work-in-progress to develop a description of the ways by which intelligence analysts engage in the thinking and reasoning processes when engaged in the intelligence analysis task. Such a model will be used to inform the design the interactive visual interfaces for a next generation intelligence analysis system. We introduce the concepts of fluidity and rigour as key characteristics of the analysts' thinking landscape.

UEcho: A Model of Uncertainty Management in Human Abductive Reasoning

Abstract: This paper explores the uncertainty aspects of human abdu c- tive reasoning. Echo, a model of abduction based on the Th e- ory of Explanatory Coherence (Thagard, 1992), captures many aspects of human abductive reasoning, but fails to su f- ficiently manage the uncertainty in abduction. In particular, Echo does not handle belief acquisition and dynamic belief revision, two essential components of human abductive re a- soning. We propose a modified Echo model (UEcho), in which we add a learning mechanism for belief acquisition and a dynamic processing mechanism for belief revision. To evaluate the model, we report an empirical study in which base rate learning serves as a testbed for belief acquisition and the order effect serves as a testbed for belief r evision.

Structural and probabilistic knowledge for abductive reasoning

Abstract: Different ways of representing probabilistic relationships among the attributes of a domain ar examined, and it is shown that the nature of domain relationships used in a representation affects the types of reasoning objectives that can be achieved. Two well-known formalisms for representing the probabilistic among attributes of a domain. These are the dependence tree formalism presented by C.K. Chow and C.N. Liu (1968) and the Bayesian networks methodology presented by J. Pearl (1986). An example is used to illustrate the nature of the relationships and the difference in the types of reasoning performed by these two representations. An abductive type of reasoning objective that requires use of the known qualitative relationships of the domain is demonstrated. A suitable way to represent such qualitative relationships along with the probabilistic knowledge is given, and how an explanation for a set of observed events may be constituted is discussed. An algorithm for learning the qualitative relationships from empirical data using an algorithm based on the minimization of conditional entropy is presented. >