Answers.Body: <p>A neural net with even a single hidden layer is capable of Universal function approximation - it can approximate any continuous function 'as closely as you like'.</p>
  
  <p>Hence, one option would be to look for GOFAI applications that would benefit from this property - for example, in state-space search approaches where the utility of a state is not readily defined in advance, and could instead be learned.</p>
  
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Answers.Text: This has actually been done, so you could edit this answer to provide a reference or link to an example of such a work.
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Answers.CreationDate: 2016-08-03T08:22:36.737Z
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Body: <p>In what ways can connectionist artificial intelligence (neural networks) be integrated with <em>Good Old-Fashioned A.I.</em> (<em>GOFAI</em>)? For instance, how could deep neural networks be integrated with knowledge bases or logical inference? One such example seems to be the <a href="http://wiki.opencog.org/w/DestinOpenCog" rel="nofollow">OpenCog + Destin integration</a>.</p>
  
Tags: <neural-networks><reference-request><symbolic-ai><symbolic-computing><neurosymbolic-ai>
Title: In what ways can connectionist AI be integrated with GOFAI?
UserDisplayName: rcpinto
ThreadText: # In what ways can connectionist AI be integrated with GOFAI?
  
  (Asked by: username_0 on 2016-08-02)
  
  In what ways can connectionist artificial intelligence (neural networks) be integrated with *Good Old-Fashioned A.I.* (*GOFAI*)? For instance, how could deep neural networks be integrated with knowledge bases or logical inference? One such example seems to be the [OpenCog + Destin integration](http://wiki.opencog.org/w/DestinOpenCog).
  
  **Tags:** neural-networks, reference-request, symbolic-ai, symbolic-computing, neurosymbolic-ai
  
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  ## 1 answers
  
  ### Answer by username_1 on 2016-08-03. Score: 1
  
  A neural net with even a single hidden layer is capable of Universal function approximation - it can approximate any continuous function 'as closely as you like'.
  
  Hence, one option would be to look for GOFAI applications that would benefit from this property - for example, in state-space search approaches where the utility of a state is not readily defined in advance, and could instead be learned.
  
  > **Comments:**
  > **username_2**: This has actually been done, so you could edit this answer to provide a reference or link to an example of such a work.  
  
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