高技术通讯2024，Vol.34Issue(3)：265-274,10.DOI:10.3772/j.issn.1002-0470.2024.03.005
基于真值表的函数自动生成的神经网络模型
Automatically generating function expressions based on truth tables with neural networks
摘要
Abstract
Programming by examples is a common program synthesis problem that involves generating programs based on input/output examples provided by users,it offers convenience for novice programmers.Recently,programming by examples has been applied to automatic programming of Microsoft Office Excel,as well as in exploration,logging,and aerospace.In order to address the gap resulting from limited research on binary data flow,the problem of gen-erating function expressions based on truth tables is introduced.This problem has the characteristic that the rela-tionship between the syntactic symbols in the sequence of symbols in the function expression is independent of their distances.Additionally,the generation of semantic rules for the function expression is unrelated to the resulting length of the Boolean vector function sampling.Based on the aforementioned characteristics,this paper introduces a neural network model and algorithm that achieve results of 70.56%,64.66%,and 0.6355 in program synthesis,functional equivalence,and sequence matching,respectively.These results outperform the existing state-of-the-art program synthesis model,which achieves 55.07%,49.70%,and 0.569 0,respectively.关键词
真值表/神经网络/序列模型/示例编程/程序综合Key words
truth table/neural network/sequential model/programming by examples/program synthesis引用本文复制引用
贺文凯,支天,胡杏,张曦珊,张蕊,杜子东,郭崎..基于真值表的函数自动生成的神经网络模型[J].高技术通讯,2024,34(3):265-274,10.基金项目
国家重点研发计划(2020AAA0103802),国家自然科学基金(61925208,U20A20227,62002338,61906179,62102399,U19B2019,61732020),北京智源人工智能研究院以及北京市科技新星计划(Z191100001119093),中国科学院稳定支持基础研究领域青年团队计划(YSBR-029)和中国科学院青年创新促进会资助项目. （2020AAA0103802）
