全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量
预印 - OALib 预印本无需同行审议


An Overview of Analytical Learning: Explanation Based Learning

DOI: 10.4236/oalib.preprints.1200295, PP. 1-8

Subject Areas: Mathematical Logic and Foundation of Mathematics, Applied Statistical Mathematics, Fuzzy Mathematics, Mathematical Statistics, Discrete Mathematics, Big Data Search and Mining, Artificial Intelligence

Keywords: Mathematical Logic, Theorem-Proving System, Machine Learnings, Prolog, Analytical Learning, Explanation Based Learning

Full-Text   Cite this paper   Add to My Lib

Abstract

Learning methods such as neural network and decision tree need a certain number of training samples to achieve a certain level of generalization accuracy. Analytic learning uses prior knowledge and deductive reasoning to expand the information provided by training examples, so it is not restricted by the same boundaries. This paper introduces an analytic learning method called explanation based learning (EBL). In interpretive learning, prior knowledge is used to analyze how the observed learning examples satisfy the goal concept. This explanation is then used to distinguish between the relevant and unrelated features in the training samples. In this way, the examples can be generalized based on logical reasoning rather than statistical reasoning. This interpretation can make the learners have higher accuracy than relying on data alone. Starting from Prolog-EBG, this paper first introduces the general characteristics of this algorithm and the relationship between other inductive learning algorithms. Finally, the application of interpretive learning to improve the performance of large state space search is described.

Cite this paper

Zhou, M. (2021). An Overview of Analytical Learning: Explanation Based Learning. Open Access Library PrePrints, 5, e295. doi: http://dx.doi.org/10.4236/oalib.preprints.1200295.

References

[1]  黄嘉星. 基于知识的神经网络在软件项目风险分析中的研究与应用[D]:[硕士学位论文]. 广州:中山大学. 2008.
[2]  李鑫. 具有成长性的人机对弈系统的研究[D]:[硕士学位论文]. 上海:上海交通大学. 2009.
[3]  陶嘉, 黎夏, 刘小平,等. 分析学习智能元胞自动机及优化的城市模拟[J]. 地理与地理信息科学, 2007, 23(5):43-47.
[4]  张谦. 基于电子商务环境的多Agent并发协商策略研究[D]:[硕士学位论文]. 重庆:西南大学. 2006.
[5]  余斌. Multi-Agent研究与应用[D]:[硕士学位论文]. 合肥:安徽大学. 2006.
[6]  施文武. 知识化制造系统中基于自学习的生产运作管理问题研究[D]:[硕士学位论文]. 南京:东南大学. 2006.
[7]  Mccarty L T , Kedar-Cabelli S T . Explanation-Based Generalization as Resolution Theorem Proving[J]. proceedings of the fourth international workshop on machine learning, 1987.
[8]  Carbonell J G , Etzioni O , Gil Y , et al.Prodigy: An integrated architecture for planning and learning[J]. ACM SIGART Bulletin, 1991, 2(4):51-55.
[9]  Scales D J . Efficient Matching Algorithms for theSoar/OPS5 Production System[J]. Stanford Univ. KSL 86-47, 1986.
[10]  Dejong G . Explanation-Based Learning: An Alternative View[J]. Machine Learning, 1986, 1(2):145-176.
[11]  Tambe M . Eliminating Combinatorics from Production Match[J]. 1991.
[12]  Gadbois D , Miranker D P . Discovering Procedural Executions of Rule-Based Programs[C]// Proceedings of the 12th National Conference on Artificial Intelligence, Seattle, WA, USA, July 31 - August 4, 1994, Volume 1. American Association for Artificial Intelligence, 1994.
[13]  Browne J C , Emerson A , Gouda M G , et al. A new approach to modularity in rule-based programming[C]// International Conference on Tools with Artificial Intelligence. IEEE, 1994.
[14]  Greco S , Romeo M , Domenico Saccà. Evaluation of negative logic programs[M]// LOGIDATA : Deductive Databases with Complex Objects. Springer Berlin Heidelberg, 1993.
[15]  M. R. K. Krishna Rao. Incremental learning of logic programs[M]// Algorithmic Learning Theory. Springer Berlin Heidelberg, 1995.
[16]  Rao M R K K . A framework for incremental learning of logic programs[M]. Elsevier Science Publishers Ltd. 1997.
[17]  Maddouri M , Elloumi S , Jaoua A . An incremental learning system for imprecise and uncertain knowledge discovery[M]. Elsevier Science Inc. 1998.
[18]  Dhar V . A truth maintenance system for supporting constraint-based reasoning[J]. Decision Support Systems, 1989, 5(4):379-387.
[19]  Zerr F , Ganascia J G . Integrating an explanation-based learning mechanism into a general problem-solver[C]// Springer Berlin Heidelberg, 1991.
[20]  Kolodner J L . Extending Problem Solver Capabilities through Case-Based Inference[J]. Proceedings of the Fourth International Workshop on Machine Learning, 1987, 30:167-178.
[21]  Dietterich T G , Flann N S . Explanation-Based Learning and Reinforcement Learning: A Unified View[J]. Machine Learning, 1997, 28(2):169-210.
[22]  Mitchell T M , Thrun S . Explanation-Based Neural Network Learning for Robot Control[M]// Explanation-Based Neural Network Learning. Springer US, 1996.

Full-Text


comments powered by Disqus

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133

WeChat 1538708413