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Abductive Reasoning and Learning pp 281–314Cite as

Abductive Inference with Probabilistic Networks

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Part of the book series: Handbook of Defeasible Reasoning and Uncertainty Management Systems ((HAND,volume 4))

Abstract

Abduction is a form of non-deductive logical inference. Examples given by [Peirce, 1958], who is said to have coined the term “abduction”, include the following:

I once landed at a seaport in a Turkish province; and as I was walking up to the house which I was to visit, I met a man upon horseback, surrounded by four horsemen holding a canopy over his head. As the governour of the province was the only personage I could think of who would be so greatly honoured, I inferred that this was he. This was a hypothesis.

Fossils are found; say remains like those of fishes, but far in the interior of the country. To explain the phenomenon, we suppose the sea once washed over this land. This is another hypothesis.

Numberless documents and monuments refer to a conqueror called Napoleon Bonaparte. Though we have not seen him, what we have seen, namely all those documents and monuments, cannot be explained without supposing that he really existed. Hypothesis again.

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Authors and Affiliations

  1. Department of Knowledge Processing and Language Engineering, Otto-von-Guericke-University of Magdeburg, Germany

    Christian Borgelt & Rudolf Kruse

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  1. Christian Borgelt
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  2. Rudolf Kruse
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Editors and Affiliations

  1. King’s College, London, UK

    Dov M. Gabbay

  2. Otto-von-Guericke-University, Magdeburg, Germany

    Rudolf Kruse

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Borgelt, C., Kruse, R. (2000). Abductive Inference with Probabilistic Networks. In: Gabbay, D.M., Kruse, R. (eds) Abductive Reasoning and Learning. Handbook of Defeasible Reasoning and Uncertainty Management Systems, vol 4. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-1733-5_7

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  • DOI: https://doi.org/10.1007/978-94-017-1733-5_7

  • Publisher Name: Springer, Dordrecht

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