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E192-04 - Forschungsbereich Formal Methods in Systems Engineering
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Published in:
GCAI 2016. 2nd Global Conference on Artificial Intelligence
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Date (published):
2016
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Event name:
GCAI 2016 / 2nd Global Conference on Artificial Intelligence
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Event date:
29-Sep-2016 - 2-Oct-2016
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Event place:
Berlin, Germany, EU
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Number of Pages:
14
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Publisher:
EasyChair, EPiC Series in Computing / 41 / Berlin
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Peer reviewed:
Yes
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Keywords:
Automated Reasoning; Theorem Proving; Satisfiability Modulo Theories; First Order Logic; Vampire; Z3
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Abstract:
This paper introduces a new technique for reasoning with quantifiers and theories. Traditionally, first-order theorem provers (ATPs) are well suited to reasoning with first-order problems containing many quantifiers and satisfiability modulo theories (SMT) solvers are well suited to reasoning with first-order problems in ground theories such as arithmetic. A recent development in first-order theor...
This paper introduces a new technique for reasoning with quantifiers and theories. Traditionally, first-order theorem provers (ATPs) are well suited to reasoning with first-order problems containing many quantifiers and satisfiability modulo theories (SMT) solvers are well suited to reasoning with first-order problems in ground theories such as arithmetic. A recent development in first-order theorem proving has been the AVATAR architecture which uses a SAT solver to guide proof search based on a propositional abstraction of the first-order clause space. The approach turns a single proof search into a sequence of proof searches on (much) smaller sub-problems. This work extends the AVATAR approach to use a SMT solver in place of the SAT solver, with the effect that the first-order solver only needs to consider ground-theory-consistent sub-problems. The new architecture has been implemented using the Vampire theorem prover and Z3 SMT solver. Our experimental results, and the results of recent competitions, show that such a combination can be highly effective.
en
Research Areas:
Logic and Computation: 90% Computer Science Foundations: 10%