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Brownian Snake Measure-Valued Markov Decision Process

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Advances in Swarm Intelligence (ICSI 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7929))

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Abstract

This paper presents a model called Brownian snake measure-valued Markov decision process (BSMMDP) that can simulate an important characteristic of human thought, that is, when people think problems, sometimes they can suddenly connect events that are remote in space-time so as to solve problems. We also discuss how to find an (approximate) optimal policy within this framework. If Artificial Intelligence can simulate human thought, then maybe it is beneficial for its progress. BSMMDP is just following this idea, and trying to describe the talent of human mind.

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References

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Author information

Authors and Affiliations

  1. School of Information Technology, Jinling Institute of Technology, Nanjing, 211169, China

    Zhenzhen Wang

  2. School of Computer Science & Engineering, Southeast University, Nanjing, 210096, China

    Hancheng Xing

Authors
  1. Zhenzhen Wang
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  2. Hancheng Xing
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Editor information

Editors and Affiliations

  1. Key Laboratory of Machine Perception (MOE), School of lectronics Engineering and Computer Science, Department of Machine Intelligence, Peking University, 100871, Beijing, China

    Ying Tan

  2. Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, 215123, Suzhou, China

    Yuhui Shi

  3. Automation College, Harbin Engineering University, 150001, Harbin, China

    Hongwei Mo

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© 2013 Springer-Verlag Berlin Heidelberg

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Wang, Z., Xing, H. (2013). Brownian Snake Measure-Valued Markov Decision Process. In: Tan, Y., Shi, Y., Mo, H. (eds) Advances in Swarm Intelligence. ICSI 2013. Lecture Notes in Computer Science, vol 7929. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38715-9_31

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  • DOI: https://doi.org/10.1007/978-3-642-38715-9_31

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38714-2

  • Online ISBN: 978-3-642-38715-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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