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Learning Ex Nihilo

27 pagesPublished: April 27, 2020


This paper introduces, philosophically and to a degree formally, the novel concept of learn- ing ex nihilo, intended (obviously) to be analogous to the concept of creation ex nihilo. Learning ex nihilo is an agent’s learning “from nothing”, by the suitable employment of inference schemata for deductive and inductive reasoning. This reasoning must be in machine-verifiable accord with a formal proof/argument theory in a cognitive calculus (i.e., here, roughly, an intensional higher-order multi-operator quantified logic), and this reasoning is applied to percepts received by the agent, in the context of both some prior knowledge, and some prior and current interests. Learning ex nihilo is a challenge to con- temporary forms of ML, indeed a severe one, but the challenge is here offered in the spirit of seeking to stimulate attempts, on the part of non-logicist ML researchers and engineers, to collaborate with those in possession of learning-ex nihilo frameworks, and eventually attempts to integrate directly with such frameworks at the implementation level. Such integration will require, among other things, the symbiotic interoperation of state-of-the- art automated reasoners and high-expressivity planners, with statistical/connectionist ML technology.

Keyphrases: automated reasoning, High-Expressivity Planner, Hybrid AI, Multi-Operator Logic

In: Gregoire Danoy, Jun Pang and Geoff Sutcliffe (editors). GCAI 2020. 6th Global Conference on Artificial Intelligence (GCAI 2020), vol 72, pages 1--27

BibTeX entry
  author    = {Selmer Bringsjord and Naveen Sundar Govindarajulu and John Licato and Michael Giancola},
  title     = {Learning Ex Nihilo},
  booktitle = {GCAI 2020. 6th Global Conference on Artificial Intelligence (GCAI 2020)},
  editor    = {Gregoire Danoy and Jun Pang and Geoff Sutcliffe},
  series    = {EPiC Series in Computing},
  volume    = {72},
  pages     = {1--27},
  year      = {2020},
  publisher = {EasyChair},
  bibsource = {EasyChair,},
  issn      = {2398-7340},
  url       = {},
  doi       = {10.29007/ggcf}}
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