Learning of human-like algebraic reasoning using deep feedforward neural networks

doi:10.1016/j.bica.2018.07.004

	Learning of human-like algebraic reasoning using deep feedforward neural networks
	Cai, Cheng-Hao 1; Xu, Yanyan 2; Ke, Dengfeng3 ; Su, Kaile 4
发表期刊	BIOLOGICALLY INSPIRED COGNITIVE ARCHITECTURES
ISSN	2212-683X
	2018-08-01
卷号	25 页码:43-50
通讯作者	Xu, Yanyan(xuyanyan@bjfu.edu.cn)
摘要	Human-like rewriting, which is an algebraic reasoning system imitating human intelligence of problem solving, is proposed in this work. In order to imitate both learning and reasoning aspects of human cognition, a deep feedforward neural network learns from algebraic reasoning examples produced by humans and then uses learnt experiences to guide other reasoning processes. This work shows that the neural network can learn human's behaviours of solving mathematical problems, and it can indicate suitable directions of reasoning, so that intelligent and heuristic reasoning can be performed. Moreover, human-like rewriting bridges the gap between symbolic reasoning and biologically inspired machine learning. To enable the neural network to recognise patterns of symbolic expressions with non-deterministic sizes, the expressions are reduced to partial tree representations and then vectorised as numeric features. Further, the centralisation method, symbolic association vectors and rule application records are used to improve the vectorised features. With these approaches, human-like rewriting shows satisfactory performance on the tasks of solving linear equations and computing derivations and indefinite integrals.
关键词	Reasoning-based learning Deep learning Algebraic reasoning Neural network reasoning
DOI	10.1016/j.bica.2018.07.004
关键词[WOS]	TERM REWRITING-SYSTEMS
收录类别	SCI
语种	英语
资助项目	Fundamental Research Funds for the Central Universities[2016JX06] ; National Natural Science Foundation of China[61472369] ; Fundamental Research Funds for the Central Universities[2016JX06] ; National Natural Science Foundation of China[61472369]
项目资助者	Fundamental Research Funds for the Central Universities ; National Natural Science Foundation of China
WOS研究方向	Computer Science ; Neurosciences & Neurology
WOS类目	Computer Science, Artificial Intelligence ; Neurosciences
WOS记录号	WOS:000447096500006
出版者	ELSEVIER SCIENCE BV
引用统计	被引频次：3[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/28119
专题	多模态人工智能系统全国重点实验室_智能交互
通讯作者	Xu, Yanyan
作者单位	1.Univ Auckland, Dept Comp Sci, 38 Princes St, Auckland 1142, New Zealand 2.Beijing Forestry Univ, Sch Informat Sci & Technol, 35 Qing Hua East Rd, Beijing 100083, Peoples R China 3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, 95 Zhong Guan Cun East Rd, Beijing 100190, Peoples R China 4.Griffith Univ, Inst Integrated & Intelligent Syst, 170 Kessels Rd, Nathan, Qld 4111, Australia
推荐引用方式 GB/T 7714	Cai, Cheng-Hao,Xu, Yanyan,Ke, Dengfeng,et al. Learning of human-like algebraic reasoning using deep feedforward neural networks[J]. BIOLOGICALLY INSPIRED COGNITIVE ARCHITECTURES,2018,25:43-50.
APA	Cai, Cheng-Hao,Xu, Yanyan,Ke, Dengfeng,&Su, Kaile.(2018).Learning of human-like algebraic reasoning using deep feedforward neural networks.BIOLOGICALLY INSPIRED COGNITIVE ARCHITECTURES,25,43-50.
MLA	Cai, Cheng-Hao,et al."Learning of human-like algebraic reasoning using deep feedforward neural networks".BIOLOGICALLY INSPIRED COGNITIVE ARCHITECTURES 25(2018):43-50.