Abstract
The paper presents an informal overview of the Connection Method in Automated Deduction. In particular, it points out its unique advantage over competing methods which consists in its formula-orientedness. Among the consequences of this unique feature are three striking advantages, viz. uniformity (over many logics), performance (due to its extreme compactness and goal-orientedness, evidenced by the leanCoP family of provers), and a global view over the proof process (enabling a higher-level guidance of the proof search). These aspects are discussed on the basis of the extensive work accumulated in the literature about this proof method. Along this line of research we envisage a bright future for the field and point out promising directions for future research.
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Notes
- 1.
Josef Urban was so kind to run the formula with several state-of-the-art provers. E processes still 1.2 million clauses which can be reduced to 36.1 thousand by automatically learned strategies (with BliStr [39]), while Prover9 succeeds already with 3.3 thousand clauses.
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Acknowledgement
I greatly appreciate a very careful reading and many suggestions by Peter Andrews, Cezary Kaliszyk, Jens Otten and Renate Schmidt as well as helpful comments and generous information on system run-times by Josef Urban. David Plaisted independently suggested in a private communication the use of deep learning techniques in AD.
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Bibel, W. (2017). A Vision for Automated Deduction Rooted in the Connection Method. In: Schmidt, R., Nalon, C. (eds) Automated Reasoning with Analytic Tableaux and Related Methods. TABLEAUX 2017. Lecture Notes in Computer Science(), vol 10501. Springer, Cham. https://doi.org/10.1007/978-3-319-66902-1_1
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