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Causal relevance

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Reasoning with Uncertainty in Robotics (RUR 1995)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1093))

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Abstract

Concepts of causal relevance and irrelevance are readily formulated in the context of Bayes nets, but these formulations have significant shortcomings. Most importantly, they do not allow for the great variety that can be observed in the temporal configuration of causally related entities. For example, they deal awkwardly with progressive causation, where continued action of a cause continues to enhance an effect. This article discusses how such subtleties can be handled when we look beyond Bayes nets to a more fundamental structure: nature's probability tree.

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References

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Authors and Affiliations

  1. Department of Accounting and Information Systems Faculty of Management, Rutgers University, 180 University Avenue, 07102, Newark, New Jersey

    Glenn Shafer

Authors
  1. Glenn Shafer
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Editor information

Leo Dorst Michiel van Lambalgen Frans Voorbraak

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© 1996 Springer-Verlag Berlin Heidelberg

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Shafer, G. (1996). Causal relevance. In: Dorst, L., van Lambalgen, M., Voorbraak, F. (eds) Reasoning with Uncertainty in Robotics. RUR 1995. Lecture Notes in Computer Science, vol 1093. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0013960

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  • DOI: https://doi.org/10.1007/BFb0013960

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61376-3

  • Online ISBN: 978-3-540-68506-7

  • eBook Packages: Springer Book Archive

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