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Discovery and Deduction

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Discovery Science (DS 2000)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1967))

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Abstract

Deduction is usually considered to be the opposite of induc- tion. However, deduction and induction can be related in many ways. In this paper, two endeavors that try to relate discovery science and verifi- cation technology are described. The first is discovery by deduction, where attempts to find algorithms are made using verifiers. Case studies of finding algorithms for concurrent garbage collection and for mutual exclusion without semaphores are described. Superoptimization can also be classiffied as work in this field. Recent work on finding authentication protocols using a protocol veriffier is also briey surveyed. The second endeavor is discovery for deduction. This concerns the long-standing problem of finding induction formulae or loop invariants. The problem is regarded as one of learning from positive data, and the notion of safe generalization, which is commonly recognized in learning from positive data, is introduced into iterative computation of loop in- variants. The similarity between the widening operator in abstract inter- pretation and Gold’s notion of identification in the limit is also discussed.

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© 2000 Springer-Verlag Berlin Heidelberg

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Hagiya, M., Takahashi, K. (2000). Discovery and Deduction. In: Arikawa, S., Morishita, S. (eds) Discovery Science. DS 2000. Lecture Notes in Computer Science(), vol 1967. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44418-1_3

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  • DOI: https://doi.org/10.1007/3-540-44418-1_3

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41352-3

  • Online ISBN: 978-3-540-44418-3

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