Simulation of Individual Knowledge System and Its Application

Abstract

The concept of individual knowledge system (IKS) is defined from the perspective of system science. To begin with, the present paper elaborates the characteristics of IKS. Then a six-space pattern of information and knowledge for IKS is created to describe and organize knowledge, providing a computation model to analyse things. Finally, an example of stability analysis in industrial economic system is used to illustrate the feasibility and validity of IKS.

Keywords

Knowledge engineering / knowledge system / artificial intelligence

References

[1] Altman R B. Artificial intelligence (AI) systems for interpreting complex medical data sets. Clinical Pharmacology & Therapeutics, 2017, 101(5): 585-586.

[2] Bojarski M, Del T D, Dworakowski D, Firner B, Flepp B, Goyal P. End to end learning for self-driving cars, 2016

[3] Baader F, Horrocks I, Sattler U. Mechanizing Mathematical Reasoning, 2005, Berlin, Heidelberg: Springer

[4] Bertalanffy L V. General System Theory, 1968, Boston, MA: Springer

[5] Berkeley G. A treatise concerning the principles of human knowledge. Journal of Nervous & Mental Disease, 1957, 84(4): 468

[6] Calvin W H. How brains think: Evolving intelligence, then and now. Quarterly Review of Biology, 1996, 72(4): 605-606.

[7] Chiou A. A game AI production shell framework: generating AI opponents for geomorphic-isometric strategy games via modeling of expert player intuition. International Conference on Digital Interactive Media in Entertainment and Arts, 2007 84-90.

[8] Chen S, Zhang S, Shang J, Chen B, Zheng N. Braininspired cognitive model with attention for self-driving cars. IEEE Transactions on Cognitive & Developmental Systems, 2019, 11(1): 13-25.

[9] Chen Y J. Development of a method for ontologybased empirical knowledge representation and reasoning. Decision Support Systems, 2011, 50(1): 1-20.

[10] Davis E. Knowledge representation. International Encyclopedia of the Social & Behavioral Sciences (Second Edition), 2015 98-104.

[11] Fu MC. AlphaGo and Monte Carlo tree search: The simulation optimization perspective. Winter Simulation Conference, 2016 659-670.

[12] Felgenbaum E A. The art of artificial intelligence: themes and case studies of knowledge engineering. International Joint Conference on Artificial Intelligence, 1977, 47: 1014-1029.

[13] Graves A, Mohamed A R, Hinton G. Speech recognition with deep recurrent neural networks. IEEE International Conference onAcoustics, Speech and Signal Processing, 2013, 38: 6645-6649.

[14] Gao L C. Systematic Inter Duality Theory, 2010

[15] Jackson P. Introduction to expert systems. IEEE Expert, 1986, 1(1): 100-109.

[16] Kelly K. The Inevitable, 2016

[17] Kandel E. The newscience of mind and the future of knowledge. Journal of Xinjiang Normal University, 2013, 80(3): 546-560.

[18] Lukovnikov D, Fischer A, Lehmann J. Neural network-based question answering over knowledge graphs on word and character level. The 26th International Conference on World Wide Web, 2017 1211-1220.

[19] Lin Y, Liu Z, Sun M, Liu Y, Zhu X. Learning entity and relation embeddings for knowledge graph completion. The 29th AAAI Conference on Artificial Intelligence, 2015 2181-2187.

[20] Laird J E. Soar Cognitive Architecture, 2012.

[21] Lovett M C, Reder L M, Lebiere C. Miyake A, Shah P. Modeling working memory in a unified architecture: An ACT-R perspective. Models of Working Memory: Mechanisms of Active Maintenance and Executive Control, 1999 135-182.

[22] LeCun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521(7553): 436-444.

[23] Markram H. The blue brain project. Nature Reviews Neuroscience, 2006, 7(2): 153-160.

[24] Miguel A L N. Are we at risk of becoming biological digital machines?. Nature Human Behaviour, 2017, 1(1): 0008.

[25] Marcus H. Universal Artificial Intelligence, 2005, Berlin: Springer.

[26] Nebel B. Logics for knowledge representation. International Encyclopedia of the Social & Behavioral Sciences, 2001, 181(4): 9039-9041.

[27] Pujara J, Miao H, Getoor L, Cohen W. Knowledge Graph Identification, 2013, Berlin, Heidelberg: Springer.

[28] Russell S J, Norvig P. Artificial intelligence, a modern approach. Applied Mechanics & Materials, 2003, 263(5): 2829-2833.

[29] Russell B. Human Knowledge, 2009

[30] Schmidhuber J. Deep learning in neural networks: An overview. Neural Network, 2015, 61: 85-117.

[31] Sun L, Wang Y Z. A multi-attribute fusion approach extending Dempster-Shafer theory for combinatorialtype evidences. Expert Systems with Applications, 2018, 96: 218-229.

[32] Thatcher R W, Palmero-Soler E, North D M, Biver C J. Intelligence and EEG measures of information flow: Efficiency and homeostatic neuroplasticity. Scientific Reports, 2016, 6(1): 38890.

[33] Thaler S L. The emerging intelligence and its critical look at us. Journal of Near-Death Studies, 1998, 17(1): 21-29.

[34] Watt Kenneth E F, Craig P P. System stability principles. Systems Research & Behavioral Science, 1986, 3(4): 191-201.

[35] Zhang L, Wang Y Z, Chen X L. Fuzzy inference method for unconventional events scenarios based on knowledge unit. Journal of Systems Engineering, 2016, 31(6): 729-738.