On Automated Scientific Theory Formation: A Case Study using the AM Program
On Automated Scientific Theory Formation: A Case Study using the AM Program
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关于自动化科学理论形成：使用 AM 程序的案例研究
DOI：
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发表时间：
2013
期刊：
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通讯作者：
D. B. Lenatt
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文献类型：
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作者：
D. B. Lenatt
一个程序称为“AM”进行简单的数学研究，定义和学习的指导下，大量的启发式规则的新概念。这250个程序员通过一个议程机制进行通信，这是一个程序要执行的小任务的全局优先级队列，以及为什么每个任务是合理的（例如，“找到'素数'的泛化，因为'素数'被证明是如此有用的一个概念”）。每个概念都被表示为一个活跃的、结构化的知识模块。最初提供了100个非常不完整的模块，每个模块对应于一个基本的集合论概念（例如，并）。这提供了一个明确而巨大的空间，AM开始探索。在一个小时内，AM重新发现了数百个常见的概念（包括单例集，自然数，算术）和定理（例如，唯一因子分解）。由于AM定义了概念，并填充了它们的方面，它没有综合新的方法来有效地处理这些新概念。这种无能是它的主要局限。
A program called "AM" is described which carries on simple mathematics research, defining and studying new concepts under the guidance of a large body of heuristic rules. The 250 heuristics communicate via an agenda mechanism, a global priority queue of small tasks for the program to perform, and reasons why each task is plausible (for example, "Find generalizations of 'primes', because 'primes' turned out to be so useful a concept"). Each concept is represented as an active, structured knowledge module. One hundred very incomplete modules are initially supplied, each one corresponding to an elementary set-theoretic concept (for example, union). This provides a definite but immense space which AM begins to explore. In one hour, AM rediscovers hundreds of common concepts (including singleton sets, natural numbers, arithmetic) and theorems (for example, unique factorization). As AM defines concepts, and fills in their facets, it does not synthesize new heuristics for dealing effectively with those new concepts. This inability turns out to be its main limitation.