Answers.Body: <p>Today one of the challenges is learning representations/concepts that are causally invariant. Once we have good representations then we can work on the reasoning aspect. There are 2 camps of people today. One believes that symbolic manipulation cannot be achieved properly by deep networks. Hence, they separate the task of extracting a lower-dimensional representation of objects from the visual scenes from the task of reasoning with knowledge-graphs. The other camp feels that we can do end-to-end training of a neural network and it can learn how to jointly learn a good lower-dimensional representation for each symbol along with learning how to reason with them. I am no expert at this but here are a few papers that I find are worthy for you to read -</p>
  <ol>
  <li>Neuro Symbolic Concent Learner <a href="https://openreview.net/pdf?id=rJgMlhRctm" rel="nofollow noreferrer">Paper</a>, <a href="https://github.com/vacancy/NSCL-PyTorch-Release" rel="nofollow noreferrer">Code</a></li>
  <li>Learning Reasoning Strategies in End-to-End Differentiable Proving <a href="http://proceedings.mlr.press/v119/minervini20a/minervini20a.pdf" rel="nofollow noreferrer">Paper</a>, <a href="https://github.com/uclnlp/ctp" rel="nofollow noreferrer">Code</a></li>
  <li>Neuro-Symbolic Visual Reasoning: Disentangling “Visual” from “Reasoning” <a href="https://arxiv.org/pdf/2006.11524v3.pdf" rel="nofollow noreferrer">Paper</a></li>
  <li>Knowledge Infused Learning (K-IL):
  Towards Deep Incorporation of Knowledge in Deep Learning <a href="https://arxiv.org/pdf/1912.00512.pdf" rel="nofollow noreferrer">Paper</a></li>
  <li>Visual Concept-Metaconcept Learning <a href="https://arxiv.org/pdf/2002.01464.pdf" rel="nofollow noreferrer">Paper</a> <a href="http://vcml.csail.mit.edu/" rel="nofollow noreferrer">Project Page</a></li>
  <li>CVPR 2020 workshop on Neuro-Symbolic Visual Reasoning and Program Synthesis <a href="https://www.youtube.com/watch?v=Opunfo422uQ&amp;list=PLX0h2D8LfCPHtT0-u6xjYaGFPT6-3cLJ1&amp;index=1&amp;ab_channel=NSCV%2720" rel="nofollow noreferrer">youtube videos</a></li>
  </ol>
  <p>If you are looking for community maintained lists, here is one  <a href="https://github.com/ccclyu/awesome-deeplogic" rel="nofollow noreferrer">list of papers</a></p>
  
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Answers.CreationDate: 2020-12-26T04:59:32.953Z
Answers.LastActivityDate: 2020-12-26T05:57:31.500Z
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Answers.UserDisplayName: Shubham Bhardwaj
Body: <p><a href="https://fr.wikipedia.org/wiki/Jacques_Pitrat" rel="nofollow noreferrer">J. Pitrat</a> (born in 1934) was a French leading artificial intelligence scientist (the first to get a Ph.D. in France mentioning &quot;artificial intelligence&quot;). His <a href="http://bootstrappingartificialintelligence.fr/WordPress3/" rel="nofollow noreferrer">blog</a> is still online and of course refer to most of his papers (e.g. <a href="https://pdfs.semanticscholar.org/2117/9600b3f05c0af399f9acbfc6e7b6d24daf03.pdf" rel="nofollow noreferrer"><em>A Step toward an Artificial Artificial Intelligence Scientist</em></a>, etc.) and books, notably <a href="https://onlinelibrary.wiley.com/doi/book/10.1002/9780470611791" rel="nofollow noreferrer"><em>Artificial Beings: the conscience of a conscious machine</em></a> (his last book). He passed away in October 2019. I attended (and presented a <a href="http://refpersys.org/Starynkevitch-CAIA-RefPerSys-2020mar06.pdf" rel="nofollow noreferrer">talk</a>) at a <a href="https://afia.asso.fr/journee-hommage-j-pitrat/" rel="nofollow noreferrer">seminar</a> in his memory.</p>
  <p><em>What are recent AI systems or research papers related to the idea of symbolic AI, introspection, declarative metaknowledge, meta-learning, meta-rules, etc.?</em></p>
  <p>Most of those I know are more than 20 years old (e.g. Lenat <a href="https://en.wikipedia.org/wiki/Eurisko" rel="nofollow noreferrer">Eurisko</a>; I am aware of <a href="https://en.wikipedia.org/wiki/Cyc#OpenCyc" rel="nofollow noreferrer">OpenCyC</a>). I am interested in papers or systems published after 2010 (perhaps <a href="https://en.wikipedia.org/wiki/Artificial_general_intelligence" rel="nofollow noreferrer">AGI</a> papers with <em>actual</em> complex open source software prototypes).</p>
  <p>-see also the <a href="http://refpersys.org/" rel="nofollow noreferrer">RefPerSys</a> system-</p>
  
Tags: <reference-request><symbolic-ai><meta-learning><meta-rules>
Title: What are recent AI software systems and research papers close to J. Pitrat's ideas?
UserDisplayName: Basile Starynkevitch
ThreadText: # What are recent AI software systems and research papers close to J. Pitrat's ideas?
  
  (Asked by: username_0 on 2020-03-08)
  
  [J. Pitrat](https://fr.wikipedia.org/wiki/Jacques_Pitrat) (born in 1934) was a French leading artificial intelligence scientist (the first to get a Ph.D. in France mentioning "artificial intelligence"). His [blog](http://bootstrappingartificialintelligence.fr/WordPress3/) is still online and of course refer to most of his papers (e.g. [*A Step toward an Artificial Artificial Intelligence Scientist*](https://pdfs.semanticscholar.org/2117/9600b3f05c0af399f9acbfc6e7b6d24daf03.pdf), etc.) and books, notably [*Artificial Beings: the conscience of a conscious machine*](https://onlinelibrary.wiley.com/doi/book/10.1002/9780470611791) (his last book). He passed away in October 2019. I attended (and presented a [talk](http://refpersys.org/username_0-CAIA-RefPerSys-2020mar06.pdf)) at a [seminar](https://afia.asso.fr/journee-hommage-j-pitrat/) in his memory.
  
  *What are recent AI systems or research papers related to the idea of symbolic AI, introspection, declarative metaknowledge, meta-learning, meta-rules, etc.?*
  
  Most of those I know are more than 20 years old (e.g. Lenat [Eurisko](https://en.wikipedia.org/wiki/Eurisko); I am aware of [OpenCyC](https://en.wikipedia.org/wiki/Cyc#OpenCyc)). I am interested in papers or systems published after 2010 (perhaps [AGI](https://en.wikipedia.org/wiki/Artificial_general_intelligence) papers with *actual* complex open source software prototypes).
  
  -see also the [RefPerSys](http://refpersys.org/) system-
  
  **Tags:** reference-request, symbolic-ai, meta-learning, meta-rules
  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2020-12-26. Score: 1
  
  Today one of the challenges is learning representations/concepts that are causally invariant. Once we have good representations then we can work on the reasoning aspect. There are 2 camps of people today. One believes that symbolic manipulation cannot be achieved properly by deep networks. Hence, they separate the task of extracting a lower-dimensional representation of objects from the visual scenes from the task of reasoning with knowledge-graphs. The other camp feels that we can do end-to-end training of a neural network and it can learn how to jointly learn a good lower-dimensional representation for each symbol along with learning how to reason with them. I am no expert at this but here are a few papers that I find are worthy for you to read -
  
  1. Neuro Symbolic Concent Learner [Paper](https://openreview.net/pdf?id=rJgMlhRctm), [Code](https://github.com/vacancy/NSCL-PyTorch-Release)
  2. Learning Reasoning Strategies in End-to-End Differentiable Proving [Paper](http://proceedings.mlr.press/v119/minervini20a/minervini20a.pdf), [Code](https://github.com/uclnlp/ctp)
  3. Neuro-Symbolic Visual Reasoning: Disentangling “Visual” from “Reasoning” [Paper](https://arxiv.org/pdf/2006.11524v3.pdf)
  4. Knowledge Infused Learning (K-IL):
     Towards Deep Incorporation of Knowledge in Deep Learning [Paper](https://arxiv.org/pdf/1912.00512.pdf)
  5. Visual Concept-Metaconcept Learning [Paper](https://arxiv.org/pdf/2002.01464.pdf) [Project Page](http://vcml.csail.mit.edu/)
  6. CVPR 2020 workshop on Neuro-Symbolic Visual Reasoning and Program Synthesis [youtube videos](https://www.youtube.com/watch?v=Opunfo422uQ&list=PLX0h2D8LfCPHtT0-u6xjYaGFPT6-3cLJ1&index=1&ab_channel=NSCV%2720)
  
  If you are looking for community maintained lists, here is one [list of papers](https://github.com/ccclyu/awesome-deeplogic)
  
  
  ---
  
  