<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>

CC BY-SA 4.0
2021-11-08T21:28:12.740Z
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.
nbro
CC BY-SA 4.0
2021-11-08T21:59:41.067Z
@nbro Done! This is from a company I used to work for.
Oliver Mason
CC BY-SA 4.0
2021-11-08T19:38:19.077Z
2021-11-08T21:58:48.790Z
2021-11-08T21:58:48.790Z
Oliver Mason
<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>

CC BY-SA 4.0
2021-11-08T19:22:05.913Z
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".
nbro
CC BY-SA 4.0
2021-11-08T19:12:09.820Z
2021-11-08T19:17:54.240Z
2021-11-08T19:17:54.240Z
Edoardo Guerriero
<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>

<machine-learning><reference-request><symbolic-ai><expert-systems><knowledge-based-systems>
Is there any other (possibly less popular) approach to create AI apart from statistical methods?
Manish Kumar Singh
# 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


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## 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**: @username_2 Done! This is from a company I used to work for.  

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### 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".  

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