IJSSER

Title: INCORPORATING ABSTRACT KNOWLEDGE STRUCTURES IN MACHINE LEARNING: IMPROVING QUESTION ANSWERING, PROBLEM SOLVING AND TEACHING IN PERSONAL ASSISTANTS AND EDUCATIONAL SOFTWARE
Authors: John Leddo and Ivy Liang
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John Leddo1 and Ivy Liang2 1. Director of Research at MyEdMaster 2. researcher at MyEdMaster
MLA 8 Leddo, John, and Ivy Liang. "INCORPORATING ABSTRACT KNOWLEDGE STRUCTURES IN MACHINE LEARNING: IMPROVING QUESTION ANSWERING, PROBLEM SOLVING AND TEACHING IN PERSONAL ASSISTANTS AND EDUCATIONAL SOFTWARE." Int. j. of Social Science and Economic Research, vol. 6, no. 2, Feb. 2021, pp. 661-673, doi:10.46609/IJSSER.2021.v06i02.017. Accessed Feb. 2021. APA 6 Leddo, J., & Liang, I. (2021, February). INCORPORATING ABSTRACT KNOWLEDGE STRUCTURES IN MACHINE LEARNING: IMPROVING QUESTION ANSWERING, PROBLEM SOLVING AND TEACHING IN PERSONAL ASSISTANTS AND EDUCATIONAL SOFTWARE. Int. j. of Social Science and Economic Research, 6(2), 661-673. doi:10.46609/IJSSER.2021.v06i02.017 Chicago Leddo, John, and Ivy Liang. "INCORPORATING ABSTRACT KNOWLEDGE STRUCTURES IN MACHINE LEARNING: IMPROVING QUESTION ANSWERING, PROBLEM SOLVING AND TEACHING IN PERSONAL ASSISTANTS AND EDUCATIONAL SOFTWARE." Int. j. of Social Science and Economic Research 6, no. 2 (February 2021), 661-673. Accessed February, 2021. doi:10.46609/IJSSER.2021.v06i02.017.
References [1]. Aleven, V., McLaren, B. M., & Sewall, J. (2009). Scaling up programming by demonstration for intelligent tutoring systems development: An open-access website for middle-school mathematics learning. IEEE Transactions on Learning Technologies, 2(2), 64-78. [2]. Chun-Yen Chen, Dian Yu, Weiming Wen, Yi Mang Yang, Jiaping Zhang, Mingyang Zhou, Kevin Jesse, Austin Chau, Antara Bhowmick, ShreenathIyer, GirithejaSreenivasulu, Runxiang Cheng, Ashwin Bhandare, and Zhou Yu. 2018. Gunrock: Building a human-like social bot by leveraging large scale real user data. In Alexa Prize 2018. [3]. Fauvel, K., Balouek-Thomert, D., Melgar, D., Silva,P., Simonet, A.,Antoniu, G.,Costan, A., Masson, V., Parashar, M., Ivan Rodero, I., and Termier, A, (2020). A Distributed Multi-Sensor Machine Learning Approach to Earthquake Early Warning. The Thirty-Fourth AAAI Conference on Artificial Intelligence. [4]. FileCloud (2018). Top 5 Limitations of Machine Learning in an Enterprise Setting. [5]. Retrieved from https://www.getfilecloud.com/blog/2018/06/top-5-limitations-of-machine-learning-in-an-enterprise-setting/#.YB7XCuhKjIV [6]. Hong, R., Liu, W., DeLair, D. and Fenyo, D. (2020). Predicting Endometrial Cancer Subtypes and Molecular Features from Histopathology Images Using Multi-resolution Deep Learning Models. Retrieved from https://www.biorxiv.org/content/10.1101/2020.02.25.965038v1.full [7]. Leddo, J., Guo, Y., Liang, Y., Joshi, R., Liang, I., Guo, W. and Bailey, S. (2019). Artificial Intelligence and Voice-powered Electronic Textbooks. International Journal of Advanced Educational Research, 4(6), 44-49. [8]. Minsky, M. (1975). A framework for representing knowledge. In P. H. Winston (Ed.), The psychology of computer vision. New York McGraw-Hill Book. [9]. Rajpurkar, P., Robin Jia, R. &Percy Liang. P. Know what you don’t know: Unanswerable questions for SQuAD. In Association for Computational Linguistics (ACL), 2018. [10]. Schank, R. S. (1982). Dynamic Memory : A theory of reminding and learning in computers and people. New York: Cambridge University Press. [11]. Schank, R.C. and Abelson, R.P. (1977). Scripts, Plans, Goals, and Understanding. Hillsdale, NJ: Erlbaum. [12]. Schank, R.C. et al. (1975). SAM – A Story Understander, Department of Computer Science Research Report No. 46, Yale University, New Haven, CT. [13]. Tan, M., Pang, R. and Le, Q.V. (2020). EfficientDet: Scalable and Efficient Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
Abstract: Artificial intelligence (AI) and machine learning(ML) are finding their way into many applications, ranging from personal assistants to learning systems to educational software. We believe that each of these applications has inherent limitations that keep it from realizing its full potential. Personal assistants answer questions by matching queries to text strings or metadata from websites. When responses are complex, users are often given links to websites rather than answers to their queries. Many learning systems use ML algorithms that focus on statistical data analysis, thus limiting its usefulness to classification or statistical predictions. Most AI-based educational software is labor intensive to create as the underlying concept knowledge must be hard-coded into the software. The limitations that all of these applications have in common is an inability to process information (learn) and form abstract knowledge representations that support higher level cognitive tasks. The present paper describes a framework and resulting software that can read a mathematics lesson on 2-step equations that is written in English (simulating the result of a search engine retrieval), learn the underlying concepts and then apply its knowledge to answer questions, solve problems, correct the work of others who are solving similar problems, and solve problems and correct the work of others for the topic of 1-step equations, even though the material it learned made no mention of this topic.

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