Answers.Body: <p>I believe "Reinforcement Learning" is the term you are looking for (as mentioned by others as well) <em>but</em> keep in mind that the scope of your problem falls under the section of AI that is called <strong>Search</strong>.</p>
  
  <p><a href="https://hackernoon.com/search-algorithms-in-artificial-intelligence-8d32c12f6bea" rel="nofollow noreferrer">Search algorithms</a> are based upon experimenting with different <strong>actions</strong> (decisions) and selecting the one that minimizes an arbitrary <strong>cost function</strong> (reward), given the current and past problem <strong>states</strong>.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2018-10-03T23:52:03.940Z
Answers.LastActivityDate: 2018-10-03T23:52:03.940Z
Answers.UserDisplayName: pcko1
Answers.Body: <p>Near solution to your problem definition is reinforcement learning. You can define some reward using the objective function and define some possible state space for the machine and finally solve the problem by reinforcement learning techniques (near to trial and error by learning the preferences).</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2018-10-03T16:57:01.800Z
Answers.LastActivityDate: 2018-10-03T16:57:01.800Z
Answers.UserDisplayName: OmG
Answers.Body: <p>I think any learning algorithm probably uses trial and error and analysis of the results with the ultimate goal of maximizing utility.</p>
  
  <p>It seems that the recent milestones in AI fall under the general umbrella of <a href="https://en.wikipedia.org/wiki/Machine_learning" rel="nofollow noreferrer">machine learning</a>, which includes all forms of <a href="https://en.wikipedia.org/wiki/Reinforcement_learning" rel="nofollow noreferrer">reinforcement learning</a>. Essentially, any learning algorithm is using some form of statistical analysis.</p>
  
  <ul>
  <li>For an umbrella term, I've been using "learning algorithm"</li>
  </ul>
  
  <p>However, there is also a venerable history of less capable adaptive systems such as <a href="https://en.wikipedia.org/wiki/Self-organizing_network" rel="nofollow noreferrer">self-organizing networks</a>.  (See also <a href="https://en.wikipedia.org/wiki/Optimal_control" rel="nofollow noreferrer">optimal control</a>.)  </p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2018-10-03T20:05:14.147Z
Answers.LastActivityDate: 2018-10-03T20:20:49.587Z
Answers.LastEditDate: 2018-10-03T20:20:49.587Z
Answers.UserDisplayName: DukeZhou
Body: <p>Imagine a system that controls dampers in a complex vent system that has an objective to perfectly equalize the output from each vent. The system has sensors for damper position, flow at various locations and at each vent. The system is initially implemented using a rather small data set or even a formulaic algorithm to control the dampers. What if that algorithm were programmed to "try" different configurations of dampers to optimize the air flows, guided broadly by either the initial (weak) training or the formula? The system would try different configurations and learn what improved results, and what worsened results, in an effort to reduce error (differential outflow).</p>
  
  <p>What is that kind of AI system called? What is that system of learning called? Are there systems that do that currently?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-11-07T17:23:39.493Z
Comments.Text: This question is very similar to [this one](https://ai.stackexchange.com/q/8241/2444), if not an exact duplicate.
Comments.UserDisplayName: nbro
Tags: <machine-learning><reinforcement-learning><terminology><incremental-learning><ai-field>
Title: What is the name of an AI system that learns by trial and error?
UserDisplayName: SchroedingersCat
ThreadText: # What is the name of an AI system that learns by trial and error?
  
  (Asked by: username_0 on 2018-10-03)
  
  Imagine a system that controls dampers in a complex vent system that has an objective to perfectly equalize the output from each vent. The system has sensors for damper position, flow at various locations and at each vent. The system is initially implemented using a rather small data set or even a formulaic algorithm to control the dampers. What if that algorithm were programmed to "try" different configurations of dampers to optimize the air flows, guided broadly by either the initial (weak) training or the formula? The system would try different configurations and learn what improved results, and what worsened results, in an effort to reduce error (differential outflow).
  
  What is that kind of AI system called? What is that system of learning called? Are there systems that do that currently?
  
  **Tags:** machine-learning, reinforcement-learning, terminology, incremental-learning, ai-field
  
  > **Comments:**
  > **username_1**: This question is very similar to [this one](https://ai.stackexchange.com/q/8241/2444), if not an exact duplicate.  
  
  ---
  
  ## 3 answers
  
  ### Answer by username_2 on 2018-10-03. Score: 1 | Accepted answer
  
  I believe "Reinforcement Learning" is the term you are looking for (as mentioned by others as well) *but* keep in mind that the scope of your problem falls under the section of AI that is called **Search**.
  
  [Search algorithms](https://hackernoon.com/search-algorithms-in-artificial-intelligence-8d32c12f6bea) are based upon experimenting with different **actions** (decisions) and selecting the one that minimizes an arbitrary **cost function** (reward), given the current and past problem **states**.
  
  
  ---
  
  ### Answer by username_3 on 2018-10-03. Score: 2
  
  Near solution to your problem definition is reinforcement learning. You can define some reward using the objective function and define some possible state space for the machine and finally solve the problem by reinforcement learning techniques (near to trial and error by learning the preferences).
  
  
  ---
  
  ### Answer by username_4 on 2018-10-03. Score: 1
  
  I think any learning algorithm probably uses trial and error and analysis of the results with the ultimate goal of maximizing utility.
  
  It seems that the recent milestones in AI fall under the general umbrella of [machine learning](https://en.wikipedia.org/wiki/Machine_learning), which includes all forms of [reinforcement learning](https://en.wikipedia.org/wiki/Reinforcement_learning). Essentially, any learning algorithm is using some form of statistical analysis.
  
  * For an umbrella term, I've been using "learning algorithm"
  
  However, there is also a venerable history of less capable adaptive systems such as [self-organizing networks](https://en.wikipedia.org/wiki/Self-organizing_network). (See also [optimal control](https://en.wikipedia.org/wiki/Optimal_control).)
  
  
  ---
  
  