摘要翻译：
自上而下和自下而上的定理证明方法各有其优缺点。自下而上的证明程序从强冗余控制中获益，但缺乏目标导向，而自上而下的证明程序是目标导向的，但在考虑其证明长度时往往具有较弱的演算能力。为了集成这两种方法，我们试图以两种不同的方式实现自顶向下和自底向上证明器之间的合作：第一种技术旨在用自顶向下证明器支持自底向上。自上而下的证明程序生成子目标子句，然后由自下而上的证明程序处理子句。第二种技术处理在自顶向下证明器中使用自底向上生成的引理。我们将我们的概念应用于模型消除和叠加的领域。我们讨论了我们的技术缩短证明以及以适当的方式重新排序搜索空间的能力。此外，为了识别与证明任务实际相关的子目标子句和引理，我们开发了基于相关性的过滤方法。用provers SETHEO和SPASS在问题库TPTP中进行的实验揭示了我们合作方法的高度潜力。
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英文标题：
《Cooperation between Top-Down and Bottom-Up Theorem Provers》
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作者：
M. Fuchs, D. Fuchs
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最新提交年份：
2011
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  Top-down and bottom-up theorem proving approaches each have specific advantages and disadvantages. Bottom-up provers profit from strong redundancy control but suffer from the lack of goal-orientation, whereas top-down provers are goal-oriented but often have weak calculi when their proof lengths are considered. In order to integrate both approaches, we try to achieve cooperation between a top-down and a bottom-up prover in two different ways: The first technique aims at supporting a bottom-up with a top-down prover. A top-down prover generates subgoal clauses, they are then processed by a bottom-up prover. The second technique deals with the use of bottom-up generated lemmas in a top-down prover. We apply our concept to the areas of model elimination and superposition. We discuss the ability of our techniques to shorten proofs as well as to reorder the search space in an appropriate manner. Furthermore, in order to identify subgoal clauses and lemmas which are actually relevant for the proof task, we develop methods for a relevancy-based filtering. Experiments with the provers SETHEO and SPASS performed in the problem library TPTP reveal the high potential of our cooperation approaches.
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PDF链接：
https://arxiv.org/pdf/1105.5458