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https://doi.org/10.1...arrow_drop_down
https://doi.org/10.1007/119162...
Part of book or chapter of book . 2006 . Peer-reviewed
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DBLP
Conference object . 2017
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Inductive Decidability Using Implicit Induction

Authors: Stephan Falke 0001; Deepak Kapur;

Inductive Decidability Using Implicit Induction

Abstract

Decision procedures are widely used in automated reasoning tools in order to reason about data structures. In applications, many conjectures fall outside the theory handled by a decision procedure. Often, reasoning about user-defined functions on those data structures is needed. For this, inductive reasoning has to be employed. In this work, classes of function definitions and conjectures are identified for which inductive validity can be automatically decided using implicit induction methods and decision procedures for an underlying theory. The class of equational conjectures considered in this paper significantly extends the results of Kapur & Subramaniam (CADE, 2000) [15], which were obtained using explicit induction schemes. Firstly, nonlinear conjectures can be decided automatically. Secondly, function definitions can use other defined functions in their definitions, thus allowing mutually recursive functions and decidable conjectures about them. Thirdly, conjectures can have general terms from the decidable theory on inductive positions. These contributions are crucial for successfully integrating inductive reasoning into decision procedures, thus enabling their use in push-button mode in applications including verification and program analysis.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
7
Average
Top 10%
Average
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