Towards a geometry deductive database prover

IF 1.2 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Annals of Mathematics and Artificial Intelligence Pub Date : 2023-05-24 DOI:10.1007/s10472-023-09839-0
Nuno Baeta, Pedro Quaresma
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Abstract

The Geometry Automated-Theorem-Provers (GATP) based on the deductive database method use a data-based search strategy to improve the efficiency of forward chaining. An implementation of such a method is expected to be able to efficiently prove a large set of geometric conjectures, producing readable proofs. The number of conjectures a given implementation can prove will depend on the set of inference rules chosen, the deductive database method is not a decision procedure. Using an approach based in an SQL database library and using an in-memory database, the implementation described in this paper tries to achieve the following goals. Efficiency in the management of the inference rules, the set of already known facts and the new facts discovered, by the use of the efficient data manipulation techniques of the SQL library. Flexibility, by transforming the inference rules in SQL data manipulation language queries, will open the possibility of meta-development of GATP based on a provided set of rules. Natural language and visual renderings, possible by the use of a synthetic forward chaining method. Implemented as an open source library, that will open its use by third-party programs, e.g. the dynamic geometry systems.

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一个几何演绎数据库证明器
基于演绎数据库方法的几何定理自动证明(GATP)采用基于数据的搜索策略来提高前向链的效率。这种方法的实现有望能够有效地证明大量的几何猜想,产生可读的证明。一个给定的实现可以证明的猜想的数量取决于所选择的推理规则集,演绎数据库方法不是一个决策过程。通过使用基于SQL数据库库和内存数据库的方法,本文描述的实现试图实现以下目标。通过使用SQL库的高效数据操作技术,有效地管理推理规则、已知事实集和发现的新事实。通过转换SQL数据操作语言查询中的推理规则,灵活性将打开基于提供的一组规则的GATP元开发的可能性。自然语言和视觉渲染,可能通过使用合成前向链方法。作为一个开源库实现,这将开放第三方程序的使用,例如动态几何系统。
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来源期刊
Annals of Mathematics and Artificial Intelligence
Annals of Mathematics and Artificial Intelligence 工程技术-计算机:人工智能
CiteScore
3.00
自引率
8.30%
发文量
37
审稿时长
>12 weeks
期刊介绍: Annals of Mathematics and Artificial Intelligence presents a range of topics of concern to scholars applying quantitative, combinatorial, logical, algebraic and algorithmic methods to diverse areas of Artificial Intelligence, from decision support, automated deduction, and reasoning, to knowledge-based systems, machine learning, computer vision, robotics and planning. The journal features collections of papers appearing either in volumes (400 pages) or in separate issues (100-300 pages), which focus on one topic and have one or more guest editors. Annals of Mathematics and Artificial Intelligence hopes to influence the spawning of new areas of applied mathematics and strengthen the scientific underpinnings of Artificial Intelligence.
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