What is the name of an AI system that learns by trial and error?

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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?










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    You have asked the same question twice here, just with a different title. Please don't do that. We should close one of the questions - please suggest which one.
    – Neil Slater
    1 hour ago






  • 1




    There is a subtle difference between the questions. One assumes a more traditional learning process before initial implementation and then refinement of that learning through use. The other question asks about the "trial and error" process where the machine may make random or educated guesses to try and optimize its results.
    – SchroedingersCat
    44 mins ago














up vote
2
down vote

favorite












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?










share|improve this question









New contributor




SchroedingersCat is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.















  • 1




    You have asked the same question twice here, just with a different title. Please don't do that. We should close one of the questions - please suggest which one.
    – Neil Slater
    1 hour ago






  • 1




    There is a subtle difference between the questions. One assumes a more traditional learning process before initial implementation and then refinement of that learning through use. The other question asks about the "trial and error" process where the machine may make random or educated guesses to try and optimize its results.
    – SchroedingersCat
    44 mins ago












up vote
2
down vote

favorite









up vote
2
down vote

favorite











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?










share|improve this question









New contributor




SchroedingersCat is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.











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?







machine-learning training ai-basics terminology






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SchroedingersCat is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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edited 31 mins ago









DukeZhou♦

2,91021028




2,91021028






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asked 3 hours ago









SchroedingersCat

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Check out our Code of Conduct.







  • 1




    You have asked the same question twice here, just with a different title. Please don't do that. We should close one of the questions - please suggest which one.
    – Neil Slater
    1 hour ago






  • 1




    There is a subtle difference between the questions. One assumes a more traditional learning process before initial implementation and then refinement of that learning through use. The other question asks about the "trial and error" process where the machine may make random or educated guesses to try and optimize its results.
    – SchroedingersCat
    44 mins ago












  • 1




    You have asked the same question twice here, just with a different title. Please don't do that. We should close one of the questions - please suggest which one.
    – Neil Slater
    1 hour ago






  • 1




    There is a subtle difference between the questions. One assumes a more traditional learning process before initial implementation and then refinement of that learning through use. The other question asks about the "trial and error" process where the machine may make random or educated guesses to try and optimize its results.
    – SchroedingersCat
    44 mins ago







1




1




You have asked the same question twice here, just with a different title. Please don't do that. We should close one of the questions - please suggest which one.
– Neil Slater
1 hour ago




You have asked the same question twice here, just with a different title. Please don't do that. We should close one of the questions - please suggest which one.
– Neil Slater
1 hour ago




1




1




There is a subtle difference between the questions. One assumes a more traditional learning process before initial implementation and then refinement of that learning through use. The other question asks about the "trial and error" process where the machine may make random or educated guesses to try and optimize its results.
– SchroedingersCat
44 mins ago




There is a subtle difference between the questions. One assumes a more traditional learning process before initial implementation and then refinement of that learning through use. The other question asks about the "trial and error" process where the machine may make random or educated guesses to try and optimize its results.
– SchroedingersCat
44 mins ago










2 Answers
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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).






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    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, which includes all forms of 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. (See also optimal control.)






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      2 Answers
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      2 Answers
      2






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      up vote
      2
      down vote













      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).






      share|improve this answer
























        up vote
        2
        down vote













        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).






        share|improve this answer






















          up vote
          2
          down vote










          up vote
          2
          down vote









          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).






          share|improve this answer












          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).







          share|improve this answer












          share|improve this answer



          share|improve this answer










          answered 3 hours ago









          OmG

          22227




          22227






















              up vote
              0
              down vote













              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, which includes all forms of 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. (See also optimal control.)






              share|improve this answer


























                up vote
                0
                down vote













                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, which includes all forms of 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. (See also optimal control.)






                share|improve this answer
























                  up vote
                  0
                  down vote










                  up vote
                  0
                  down vote









                  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, which includes all forms of 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. (See also optimal control.)






                  share|improve this answer














                  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, which includes all forms of 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. (See also optimal control.)







                  share|improve this answer














                  share|improve this answer



                  share|improve this answer








                  edited 4 mins ago

























                  answered 20 mins ago









                  DukeZhou♦

                  2,91021028




                  2,91021028




















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