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International Conference on Artificial General Intelligence

AGI 2018: Artificial General Intelligence pp 303–310Cite as

Adaptive Compressed Search

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10999))

Abstract

Program-search as induction and abduction is one of the key pillars of any sufficiently advanced AGI. In this paper, we present a mechanism to search for programs given a specific bias. This bias is flexible to some degree. Another novel attribute of the mechanism is the use of compression that selects simple programs over complex ones. The complexity of the program is changing all the time over the lifetime of the agent.

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Notes

  1. 1.

    The sourcecode can be found at https://github.com/PtrMan/AGIconf2018CompressedSearch.

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Author information

Authors and Affiliations

  1. Technische Universität Dresden, Dresden, Germany

    Robert Wünsche

Authors
  1. Robert Wünsche
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Correspondence to Robert Wünsche .

Editor information

Editors and Affiliations

  1. Adams State University, Alamosa, Colorado, USA

    Matthew Iklé

  2. Odessa Competence Center for Artificial Intelligence (OCCAM), Odessa, Ukraine

    Arthur Franz

  3. Hokkaido University, Sapporo, Japan

    Rafal Rzepka

  4. SingularityNET; OpenCog; Hanson Robotics, Hong Kong, China

    Ben Goertzel

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Wünsche, R. (2018). Adaptive Compressed Search. In: Iklé, M., Franz, A., Rzepka, R., Goertzel, B. (eds) Artificial General Intelligence. AGI 2018. Lecture Notes in Computer Science(), vol 10999. Springer, Cham. https://doi.org/10.1007/978-3-319-97676-1_29

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  • DOI: https://doi.org/10.1007/978-3-319-97676-1_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-97675-4

  • Online ISBN: 978-3-319-97676-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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