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The second approach simulates learning and using the skill

This approach develops skills based on experiential learning. Computer algorithms include neural networks. Robots have been built to interact with each other and to have learning capabilities.

This approach has been less successful than the first in simulating interesting and challenging skills such as playing chess or competing in Jeopardy. They do illustrate learning, but it is not proven that they simulate how humans learn. So far they have not been used extensively for diagnosing problems and helping to improve skills.

With the development of neural imaging and increasing interest in computational neuroscience, there is a lot of interest and some effort in building artificial intelligence simulations out of neural components.

Usability of the approach for skill engineering

There very few such models, and practically no experience in using them for skill engineering. Assuming that we can find a working simulation for the kind of skill we are working with, we can focus on changes in the skilled behaviour due to a sequence of experiences. We can add to the list of questions.

  1. Can we customize the learning status and learning limits for an individual on a given skill?
  2. Can we simulate the changes in a skill due to specific input such as skill applications in a given context?
  3. Can we simulate skill decay due to lack of practice, and can be simulate skill improvements?
  4. Can we predict the state of a skill after a given number of practice sessions, and can we predict the number of practice sessions needed to reach a given skill performance level?
  5. Can we calculate the optimum type and sequence of practice sessions to reach a given skill level for a particular individual?