This approach focuses on a relatively static version of the skill or skill set. Passing a Turing test is a reasonable goal for this approach. The Turing test requires that observers cannot readily tell whether the behaviour originates from a real person or from a computer simulation. We look for algorithms that the computer or robot can use to produce not just a single skilled behaviour but a set of behaviours that are appropriate in different contexts and circumstances.
The robot will try to recognize the context and produce physical behaviour that is similar to that of a human. The computer will start with a symbolic description of the context and produce a verbal (or mathematical) specification for the behaviour. The computer, therefore, contains the computer and extends its capabilities to be more life-like by including perception and physical action. The robot is more labour-intensice and expensive to produce.
This approach has been quite successful but is rather limited in supporting skill engineering. We have programs that play chess very well (e.g. IBM's Deep Blue), but they do not simulate how humans play chess, and they cannot teach chess by diagnosing problems and helping to improve the skill.
The approach supports predictions that help control skilled behaviour by adjusting the context, i.e. the input. The approach is not sufficient for working with skill development since it simulates a static version if the skill, i.e. a skill that does not improve with experience, it does not learn.
There is a variety of such models, but only a limited amount of experience in using them for skill engineering. We start with the assumption that we have a working simulation, and go through the questions we are trying to address with the simulation.