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A Formal Model for Robot to Understand Common Concepts

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Intelligent Computing (CompCom 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 997))

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Abstract

Can the robot think? This is a major topic of artificial intelligence and has always attracted the attention and exploration of many researchers. Since Searle proposed the Chinese House Thought Experiment in 1980, researchers have conducted extensive research on whether or not a formal symbolic system itself can understand the meaning of symbols, and have obtained many insights. From the perspective of cognitive science, this paper studies how robots understand knowledge, and proposes a general model that expresses robots’ understanding of common concepts (the simplest form of knowledge) to describe the process by which robots understand some simple knowledge. We actually find a new way for robots to understand complex knowledge. In this paper, we also illustrate our method by an example of robot’s understanding the concept of “pen”.

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References

  1. Searle, J.R.: Minds, brains, and programs. Behav. Brain Sci. 3, 417–457 (1980)

    Article  Google Scholar 

  2. Yoshimura, E., Imono, M., Tsuchiya, S., Watabe, H.: A simile recognition system using a commonsense sensory association method. Procedia Comput. Sci. 60, 55–62 (2015)

    Article  Google Scholar 

  3. Zhou, Z., Li, K., Bai, L.: A general description generator for human activity images based on deep understanding framework. Neural Comput. Appl. 28(8), 2147–2163 (2017)

    Article  Google Scholar 

  4. Terai, A., Nakagawa, M.: A corpus-based computational model of metaphor understanding consisting of two processes. Cogn. Syst. Res. 19–20, 30–38 (2012)

    Article  Google Scholar 

  5. Doboli, A., Umbarkar, A., Doboli, S., Bets, J.: Modeling semantic knowledge structures for creative problem solving: studies on expressing concepts, categories, associations, goals and context. Knowl.-Based Syst. 78, 34–50 (2015)

    Article  Google Scholar 

  6. Roy, R.S., Katare, R., Ganguly, N., Laxman, S., Choudhury, M.: Discovering and understanding word level user intent in Web search queries. Web Semant.: Sci. Serv. Agents World Wide Web 30, 22–38 (2015)

    Article  Google Scholar 

  7. Sheng, W., Du, J., Cheng, Q., Li, G., Zhu, C., Liu, M.: Robot semantic mapping through human activity recognition: a wearable sensing and computing approach. Robot. Auton. Syst. 68, 47–58 (2015)

    Article  Google Scholar 

  8. Ramirez-Amaro, K., Beetz, M., Cheng, G.: Transferring skills to humanoid robots by extracting semantic representations from observations of human activities. Artif. Intell. 247, 95–118 (2017)

    Article  MathSciNet  Google Scholar 

  9. Khalifa, K., Gadomski, M.: Understanding as explanatory knowledge: the case of Bjorken scaling. Stud. Hist. Philos. Sci. 44, 384–392 (2013)

    Article  Google Scholar 

  10. Zhong, Y.: Artificial intelligence: concepts, methods and opportunities. Chin. Sci. Bull. 62(22), 2473–2479 (2017)

    Article  Google Scholar 

  11. She, Z.-S., Ni, Z.: On complex system model of thinking and design of a new generation of expert s ystem. Acta Sci. Natur. Univ. Pekinensis 47(5), 960–968 (2011)

    Google Scholar 

  12. He, C.-X., Li, X.-W.: Logical characterization: a new perspective of studying “understanding”. J. Xiamen Univ. (Philos. Soc. Sci. Edn.) 4, 28–34 (2010)

    Google Scholar 

  13. Xia, Y.-H., Li, J.: The problem of symbol laying foundation and its solving strategy. Philos. Res. 2, 102–110 (2017)

    Google Scholar 

  14. Jiang, J.-T.: From the Chinese room thought experiment to the problem of intentionality. Philos. Sci. Technol. 30(3), 40–44 (2013)

    Google Scholar 

  15. Fan, Y.-H.: Understanding: a linguistic phenomenon - the revision of Chinese Room thought experiment from Wittgenstein’s point of view. J. Dialect. Nat. 36(4), 44–50 (2014)

    Google Scholar 

  16. Chen, J.-M.: Knowledge theory of “understanding”. Philos. Trends 11, 69–75 (2016)

    Google Scholar 

  17. Retire, R.: Knowledge in Action: Logical Foundations for Specifying and Implementing Dynamical Systems. MIT Press, Cambridge (2001)

    Google Scholar 

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Acknowledgments

This paper is supported by the National Natural Science Foundation of China (Nos. 61662007, 61762015, and 61762016).

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Authors and Affiliations

  1. Guangxi Key Laboratory of Multi-source Information Mining and Safety, Guangxi Regional Multi-source Information Integration and Intelligent Processing Collaborative Innovation Center, Guangxi Normal University, Guilin, 541004, Guangxi, China

    Yuanxiu Liao, Jingli Wu & Xudong Luo

Authors
  1. Yuanxiu Liao
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  2. Jingli Wu
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  3. Xudong Luo
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Corresponding author

Correspondence to Xudong Luo .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information SAI Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

  3. The Science and Information SAI Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

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Liao, Y., Wu, J., Luo, X. (2019). A Formal Model for Robot to Understand Common Concepts. In: Arai, K., Bhatia, R., Kapoor, S. (eds) Intelligent Computing. CompCom 2019. Advances in Intelligent Systems and Computing, vol 997. Springer, Cham. https://doi.org/10.1007/978-3-030-22871-2_35

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