Advertisement

From Abstract Agents Models to Real-World AGI Architectures: Bridging the Gap

  • Ben Goertzel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10414)

Abstract

A series of formal models of intelligent agents is proposed, with increasing specificity and complexity: simple reinforcement learning agents; “cognit” agents with an abstract memory and processing model; hypergraph-based agents (in which “cognit” operations are carried out via hypergraphs); hypergraph agents with a rich language of nodes and hyperlinks (such as the OpenCog framework provides); “PGMC” agents whose rich hypergraphs are endowed with cognitive processes guided via Probabilistic Growth and Mining of Combinations; and finally variations of the PrimeAGI design, which is currently being built on top of the OpenCog framework.

References

  1. 1.
    Baget, J.F., Mugnier, M.L.: Extensions of simple conceptual graphs: the complexity of rules and constraints. J. Artif. Intell. Res. 16, 425–465 (2002)MathSciNetzbMATHGoogle Scholar
  2. 2.
    Goertzel, B., Ikle, M., Goertzel, I., Heljakka, A.: Probabilistic Logic Networks. Springer, Heidelberg (2008)zbMATHGoogle Scholar
  3. 3.
    Goertzel, B.: Chaotic Logic. Plenum, New York (1994)CrossRefzbMATHGoogle Scholar
  4. 4.
    Goertzel, B.: Toward a formal definition of real-world general intelligence. In: Proceedings of AGI 2010 (2010)Google Scholar
  5. 5.
    Goertzel, B.: Probabilistic growth and mining of combinations: a unifying meta-algorithm for practical general intelligence. In: Steunebrink, B., Wang, P., Goertzel, B. (eds.) AGI -2016. LNCS, vol. 9782, pp. 344–353. Springer, Cham (2016). doi: 10.1007/978-3-319-41649-6_35 Google Scholar
  6. 6.
    Goertzel, B.: Cost-based intuitionist probabilities on spaces of graphs, hypergraphs and theorems (2017)Google Scholar
  7. 7.
    Goertzel, B.: Toward a formal model of cognitive synergy. In: Proceedings of AGI 2017. Springer, Cham (2017, submitted)Google Scholar
  8. 8.
    Goertzel, B.: Toward a formal model of cognitive synergy (2017). https://arxiv.org/abs/1703.04361
  9. 9.
    Goertzel, B., Pennachin, C., Geisweiller, N.: Engineering General Intelligence, Part 1: A Path to Advanced AGI via Embodied Learning and Cognitive Synergy. Atlantis Thinking Machines, New York (2013). SpringerGoogle Scholar
  10. 10.
    Goertzel, B., Pennachin, C., Geisweiller, N.: Engineering General Intelligence, Part 2: The CogPrime Architecture for Integrative, Embodied AGI. Atlantis Thinking Machines, New York (2013). SpringerGoogle Scholar
  11. 11.
    Hutter, M.: Universal Artificial Intelligence: Sequential Decisions based on Algorithmic Probability. Springer, Heidelberg (2005)CrossRefzbMATHGoogle Scholar
  12. 12.
    Legg, S.: Machine super intelligence. Ph.D. thesis, University of Lugano (2008)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.OpenCog FoundationSha TinHong Kong

Personalised recommendations