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Adaptive Neuro-Symbolic Network Agent

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Artificial General Intelligence (AGI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11654))

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Abstract

This paper describes Adaptive Neuro-Symbolic Network Agent, a new design of a sensorimotor agent that adapts to its environment by building concepts based on Sparse Distributed Representations of sensorimotor sequences. Utilizing Non-Axiomatic Reasoning System theory, it is able to learn directional correlative links between concept activations that were caused by the appearing of observed and derived event sequences. These directed correlations are encoded as predictive links between concepts, and the system uses them for directed concept-driven activation spreading, prediction, anticipatory control, and decision-making, ultimately allowing the system to operate autonomously, driven by current event and concept activity, while working under the Assumption of Insufficient Knowledge and Resources.

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Notes

  1. 1.

    A detail: As in [8], only revise if the evidential base does not overlap, and only if the revised element when projected to the occurrence-time middle between both elements is higher than the premises’s.

  2. 2.

    Which generates an Anticipation, that if it won’t get confirmed, adds negative evidence to the implication (predictive link) that generated the prediction (as AERA).

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Author information

Authors and Affiliations

  1. Department of Computer and Information Sciences, College of Science and Technology, Temple University, Philadelphia, PA, 19122, USA

    Patrick Hammer

Authors
  1. Patrick Hammer
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Correspondence to Patrick Hammer .

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Editors and Affiliations

  1. Temple University, Philadelphia, PA, USA

    Patrick Hammer

  2. University of Memphis, Memphis, TN, USA

    Pulin Agrawal

  3. SingularityNET, Tai Po, Hong Kong, China

    Ben Goertzel

  4. SingularityNET, Alamosa, CO, USA

    Matthew Iklé

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Hammer, P. (2019). Adaptive Neuro-Symbolic Network Agent. In: Hammer, P., Agrawal, P., Goertzel, B., Iklé, M. (eds) Artificial General Intelligence. AGI 2019. Lecture Notes in Computer Science(), vol 11654. Springer, Cham. https://doi.org/10.1007/978-3-030-27005-6_8

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  • DOI: https://doi.org/10.1007/978-3-030-27005-6_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-27004-9

  • Online ISBN: 978-3-030-27005-6

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