Answers.Body: <p>Yes, there is <em><a href="https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence" rel="nofollow noreferrer">symbolic AI</a></em>. This was the 'original' approach to AI, at a time when there was very little data and/or processing power available. The focus was on logic and calculus, not on machine learning, which was just in its infancy.</p>
  <p>A lot of natural language processing was developed using grammar rules (which only later were learned from data).</p>
  <p>There still is a lot of this around, but often it's now hybrid, where human-authored rules correct the systematic mistakes of statistical approaches.</p>
  <p>Update: as an example the Teneo system for building conversational agents (aka chatbots) uses both pattern matching rules and machine learning for intent recognition. The (human-created) patterns are more precise, but sometimes lack breadth of coverage, which the ML part provides, which works as a fallback.</p>
  
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Answers.Text: It would be interesting if you could provide an example of a system that is hybrid, where the rules correct the statistical approaches. I know that some cognitive architectures are hybrid, but I don't remember the details or names. [This info](https://researcher.watson.ibm.com/researcher/view_group.php?id=10518) could be useful to answer this question.
Answers.UserDisplayName: nbro
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Answers.Text: @nbro Done! This is from a company I used to work for.
Answers.UserDisplayName: Oliver Mason
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Answers.CreationDate: 2021-11-08T19:38:19.077Z
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Answers.LastEditDate: 2021-11-08T21:58:48.790Z
Answers.UserDisplayName: Oliver Mason
Answers.Body: <p>What you're looking for are <a href="https://en.wikipedia.org/wiki/Expert_system" rel="nofollow noreferrer">Expert systems</a> and <a href="https://en.wikipedia.org/wiki/Knowledge-based_systems" rel="nofollow noreferrer">Knowledge Based Systems</a>. Really similar to each other, they encompass all systems built upon experts knowledge, from which analytic rules are derived in order to allow their implementation in computer programs.</p>
  <p>A trivial example could be a self driving system based on proximity sensors, a camera and a set of predefined rules (basically a set of explicit if-else statements) like:</p>
  <ul>
  <li>if value recorder from front proximity sensor &lt; 10cm &amp; left bottom corner of picture from camera is white (wall on the left) -&gt; turn wheels 90 degrees to the right.</li>
  <li>if value recorder from front proximity sensor &lt; 10cm &amp; right bottom corner of picture from camera is white -&gt; turn wheels 90 degrees to the left.</li>
  <li>... (and so on and do forth)</li>
  </ul>
  
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Answers.Text: I wouldn't limit the answer to this question to knowledge-based systems, but to **symbolic AI** (aka classical AI or GOFAI), which is the alternative approach to "statistical AI". This answer may not explicitly suggest that, for example, search algorithms (like A*) can be considered "AI algorithms".
Answers.UserDisplayName: nbro
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Answers.LastEditDate: 2021-11-08T19:17:54.240Z
Answers.UserDisplayName: Edoardo Guerriero
Body: <p>From what I have gathered so far, an AI has some prior (stored in the form of some probability distribution), and, based on experiences/data, changes the distribution (via Bayes rule) accordingly. This idea seems intuitively correct, as humans do something similar: we have some prejudice about certain things and refine it further based on additional observations.</p>
  <p>I am wondering if there is a different (possibly, non-probabilistic) setting for designing an AI.</p>
  
Tags: <machine-learning><reference-request><symbolic-ai><expert-systems><knowledge-based-systems>
Title: Is there any other (possibly less popular) approach to create AI apart from statistical methods?
UserDisplayName: Manish Kumar Singh
ThreadText: # Is there any other (possibly less popular) approach to create AI apart from statistical methods?
  
  (Asked by: username_0 on 2021-11-08)
  
  From what I have gathered so far, an AI has some prior (stored in the form of some probability distribution), and, based on experiences/data, changes the distribution (via Bayes rule) accordingly. This idea seems intuitively correct, as humans do something similar: we have some prejudice about certain things and refine it further based on additional observations.
  
  I am wondering if there is a different (possibly, non-probabilistic) setting for designing an AI.
  
  **Tags:** machine-learning, reference-request, symbolic-ai, expert-systems, knowledge-based-systems
  
  ---
  
  ## 2 answers
  
  ### Answer by username_1 on 2021-11-08. Score: 2
  
  Yes, there is *[symbolic AI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)*. This was the 'original' approach to AI, at a time when there was very little data and/or processing power available. The focus was on logic and calculus, not on machine learning, which was just in its infancy.
  
  A lot of natural language processing was developed using grammar rules (which only later were learned from data).
  
  There still is a lot of this around, but often it's now hybrid, where human-authored rules correct the systematic mistakes of statistical approaches.
  
  Update: as an example the Teneo system for building conversational agents (aka chatbots) uses both pattern matching rules and machine learning for intent recognition. The (human-created) patterns are more precise, but sometimes lack breadth of coverage, which the ML part provides, which works as a fallback.
  
  > **Comments:**
  > **username_2**: It would be interesting if you could provide an example of a system that is hybrid, where the rules correct the statistical approaches. I know that some cognitive architectures are hybrid, but I don't remember the details or names. [This info](https://researcher.watson.ibm.com/researcher/view_group.php?id=10518) could be useful to answer this question.  
  > **username_1**: @nbro Done! This is from a company I used to work for.  
  
  ---
  
  ### Answer by username_3 on 2021-11-08. Score: 1
  
  What you're looking for are [Expert systems](https://en.wikipedia.org/wiki/Expert_system) and [Knowledge Based Systems](https://en.wikipedia.org/wiki/Knowledge-based_systems). Really similar to each other, they encompass all systems built upon experts knowledge, from which analytic rules are derived in order to allow their implementation in computer programs.
  
  A trivial example could be a self driving system based on proximity sensors, a camera and a set of predefined rules (basically a set of explicit if-else statements) like:
  
  * if value recorder from front proximity sensor < 10cm & left bottom corner of picture from camera is white (wall on the left) -> turn wheels 90 degrees to the right.
  * if value recorder from front proximity sensor < 10cm & right bottom corner of picture from camera is white -> turn wheels 90 degrees to the left.
  * ... (and so on and do forth)
  
  > **Comments:**
  > **username_2**: I wouldn't limit the answer to this question to knowledge-based systems, but to **symbolic AI** (aka classical AI or GOFAI), which is the alternative approach to "statistical AI". This answer may not explicitly suggest that, for example, search algorithms (like A*) can be considered "AI algorithms".  
  
  ---
  
  