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Abstract: . Proof planning is an application of Artificial Intelligence (AI)-planning in mathematical domains for theorem proving. The paper presents a knowledge-based proof planning approach that is implemented in the OMEGA proof planner. It evaluates control-rules in order to restrict the otherwise intractable search spaces and combines proof planning with domain-specific constraint solving. Several AI-techniques contribute to the successful planning of proofs that were beyond the capabilities of... (Update)
Context of citations to this paper: More
...the meta level reasoning that guides the selection of methods. Currently, this meta level reasoning is encoded by a list of control rules [13] or by variations of the difference reduction search heuristic called rippling [7] However, when the class of theorems to be proved with...
...3.1. Hierarchical Proof Plan Data Structure Finding a proof with mega can be viewed as a process that interleaves proof planning [Mel98, CS98] plan execution, and veri cation, all of which is datadriven by the so called Proof Plan Data Structure (PDS) This hierarchical...
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Extensions of Constraint Solving for Proof Planning - Melis, Zimmer, Müller (1999) (Correct)
Integrating Constraint Solving into Proof Planning - Melis, Zimmer, Müller (Correct)
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7: The Oz programming model - Smolka - 1995
6: The use of explicit plans to guide inductive proofs - Bundy - 1988
5: Argonne National Laboratory (context) - McCune - 1994
BibTeX entry: (Update)
Melis, E. (1998). AI-techniques in proof planning. In European Conference on Artificail Intelligence, 1998. http://citeseer.ist.psu.edu/melis98aitechniques.html More
@inproceedings{ melis98aitechniques,
author = "Erica Melis",
title = "{AI}-Techniques in Proof Planning",
booktitle = "European Conference on Artificial Intelligence",
pages = "494-498",
year = "1998",
url = "citeseer.ist.psu.edu/melis98aitechniques.html" }
Citations (may not include all citations):891 STRIPS: A new approach to the application of theorem proving.. (context) - Fikes, Nilsson - 1971
380 Constraint Satisfaction in Logic Programming (context) - Van Hentenryck - 1989
226 The use of explicit plans to guide inductive proofs - Bundy - 1988
150 Edinburgh LCF: A Mechanized Logic of Computation (context) - Gordon, Milner et al. - 1979
97 Experiments with proof plans for induction - Bundy, van Harmelen et al. - 1991
62 Solution of the Robbins problem - McCune - 1997
55 A science of reasoning - Bundy - 1991
42 A model of analogy-driven proof-plan construction - Melis - 1995
26 The CLP(R) language and system (context) - Jaffar, Michaylow et al. - 1992
23 Let's plan it deductively - Bibel - 1997
23 Computer proofs of limit theorems (context) - Bledsoe, Boyer et al. - 1972
20 Membership constraints and complexity in logic programming w.. - Stolzenburg - 1996
14 Abstract planning and perceptual chunks: Elements of experti.. (context) - Koedinger, Anderson - 1990
14 Coloured rippling: An extension of a theorem proving heurist.. - Tetsuya, Bundy et al. - 1994
9 OMEGA: Towards a mathematical assistant (context) - Benzmueller, Cheikhrouhou et al. - 1997
8 The Manual and Tutorial (context) - Minton, Knoblock et al. - 1989
8 Challenge problems in elementary analysis (context) - Bledsoe - 1990
6 Acquisition of proof skills in geometry (context) - Anderson - 1984
5 Otter 2.0 users guide (context) - McCune - 1990
2 Progress in proof planning: Planning limit theorems automati.. - Melis - 1997
1 Str+veae: The stri+ve-based subset prover (context) - Hines - 1990
The graph only includes citing articles where the year of publication is known.
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Preferential Semantics for Goals - Wellman, Doyle (1991) (Correct)
Planning in a Complex Real Domain - Ricci, Perini, Avesani (1993) (Correct)
Lazy Incremental Learning of Control Knowledge for.. - Borrajo, Veloso (1996) (Correct)
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