Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Perception from an AGI Perspective

  • Conference paper
  • First Online:
Artificial General Intelligence (AGI 2018)

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

Included in the following conference series:

  • 1492 Accesses

Abstract

This paper argues that according to the relevant discoveries of cognitive science, in AGI systems perception should be subjective, active, and unified with other processes. This treatment of perception is fundamentally different from the mainstream approaches in computer vision and machine learning, where perception is taken to be objective, passive, and modular. The conceptual design of perception in the AGI system NARS is introduced, where the three features are realized altogether. Some preliminary testing cases are used to show the features of this novel approach.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 49.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 64.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Aloimonos, J., Weiss, I., Bandyopadhyay, A.: Active vision. Int. J. Comput. Vis. 1(4), 333–356 (1988)

    Article  Google Scholar 

  2. Barsalou, L.W.: Perceptual symbol systems. Behav. Brain Sci. 22, 577–609 (1999)

    Google Scholar 

  3. Brette, R.: Subjective physics. arXiv:1311.3129v1 [q-bio.NC] (2013)

  4. Chalmers, D.J., French, R.M., Hofstadter, D.R.: High-level perception, representation, and analogy: a critique of artificial intelligence methodology. J. Exp. Theor. Artif. Intell. 4, 185–211 (1992)

    Article  Google Scholar 

  5. Di Paolo, E.A., Barandiaran, X.E., Beaton, M., Buhrmann, T.: Learning to perceive in the sensorimotor approach: Piaget’s theory of equilibration interpreted dynamically. Front. Hum. Neurosci. 8, 551 (2014)

    Article  Google Scholar 

  6. Flach, P.: Machine Learning: The Art and Science of Algorithms That Make Sense of Data. Cambridge University Press, New York (2012)

    Book  Google Scholar 

  7. Gibson, J.J.: The theory of affordances. In: The Ecological Approach To Visual Perception, New edn. Chap. 8, pp. 127–143. Psychology Press (1986)

    Google Scholar 

  8. Goldstone, R.L., Barsalou, L.W.: Reuniting perception and conception. Cognition 65, 231–262 (1998)

    Article  Google Scholar 

  9. Hatfield, G.: Perception as unconscious inference. In: Heyer, D., Mausfeld, R. (eds.) Perception and the Physical World: Psychological and Philosophical Issues in Perception, pp. 113–143. Wiley, New York (2002)

    Google Scholar 

  10. Hockema, S.A.: Perception as prediction. In: Proceedings of the Cognitive Science conference (2004)

    Google Scholar 

  11. Hommel, B., Müsseler, J., Aschersleben, G., Prinz, W.: The theory of event coding (TEC): a framework for perception and action planning. Behav. Brain Sci. 24(5), 849–78 (2001)

    Article  Google Scholar 

  12. Jarvilehto, T.: Efferent influences on receptors in knowledge formation. Psycoloquy 9(41), Article 1 (1998)

    Google Scholar 

  13. Lakoff, G., Johnson, M.: Philosophy in the Flesh: The Embodied Mind and Its Challenge to Western Thought. Basic Books, New York (1998)

    Google Scholar 

  14. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015)

    Article  Google Scholar 

  15. Marr, D.: Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. W. H. Freeman & Co., San Francisco (1982)

    Google Scholar 

  16. Noë, A.: Action in Perception. MIT Press, Cambridge (2004)

    Google Scholar 

  17. O’Regan, J., Noë, A.: A sensorimotor account of vision and visual consciousness. Behav. Brain Sci. 24(05), 939–973 (2001)

    Article  Google Scholar 

  18. Piaget, J.: The Construction of Reality in the Child. Basic Books, New York (1954)

    Book  Google Scholar 

  19. Rock, I.: The Logic of Perception. MIT Press, Cambridge (1983)

    Google Scholar 

  20. Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach, 3rd edn. Prentice Hall, Upper Saddle River (2010)

    MATH  Google Scholar 

  21. Santoro, A., et al.: A simple neural network module for relational reasoning. CoRR abs/1706.01427 (2017), http://arxiv.org/abs/1706.01427

  22. Shams, L., Shimojo, S.: Sensory modalities are not separate modalities: plasticity and interactions. Curr. Opin. Neurobiol. 1, 505–509 (2001)

    Google Scholar 

  23. Shanahan, M.: Perception as abduction: turning sensor data into meaningful representation. Cogn. Sci. 29(1), 103–134 (2005)

    Article  Google Scholar 

  24. Wang, P.: Experience-grounded semantics: a theory for intelligent systems. Cogn. Syst. Res. 6(4), 282–302 (2005)

    Article  Google Scholar 

  25. Wang, P.: Rigid Flexibility: The Logic of Intelligence. Springer, Dordrecht (2006). https://doi.org/10.1007/1-4020-5045-3

    Book  MATH  Google Scholar 

  26. Wang, P.: Solving a problem with or without a program. J. Artif. Gen. Intell. 3(3), 43–73 (2012)

    Google Scholar 

  27. Wang, P.: Non-Axiomatic Logic: A Model of Intelligent Reasoning. World Scientific, Singapore (2013)

    Book  Google Scholar 

  28. Wu, T.: Integration and goal-guided scheduling of bottom-up and top-down computing processes in hierarchical models. Ph.D. thesis, University of California, Los Angeles (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer and Information Sciences, Temple University, Philadelphia, USA

    Pei Wang & Patrick Hammer

Authors
  1. Pei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Patrick Hammer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pei Wang .

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

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wang, P., Hammer, P. (2018). Perception from an AGI Perspective. 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_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-97676-1_25

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-97675-4

  • Online ISBN: 978-3-319-97676-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation