PhD Document - Universidad de Las Palmas de Gran Canaria

CHAPTER 2. THE CASE OF SOCIAL ROBOTICS
is valid in predicting future cases only as long as we are capturing knowledge about the
solution, whether intentionally or not.
The conclusion is: in any attempt to reproduce a social ability in a certain domain
there is a serious risk of overfitting to the test cases. This means that in practice we will have
a situation like that depicted in Figure 2.3.
Original problem
to solve
Original domain
Pick
2
Low
error
Figure 2.3: Situation when there is little knowledge about the form of the solution. We can
be in one of three possibilities: 1) have low error in a domain different than the original, 2)
work in the original domain but with large error, or 3) have low error in the original domain
but in that case solving a problem different than the original.
The reasoning path taken in this chapter has been the following. First, instead of
autism, the condition of autistic savants was seen as more analogous to the social robots that
we build, based on the unbalanced intelligences and capacities that are observed. Studies on
the origin of that condition show that there are unconscious mental processes that provide
healthy adults with the final useful products of reasoning: concepts. Finally, unconscious
mental processes, useful as they are, are formally demonstrated to hinder our capacity to
reproduce some of our own social capacities. Based on these ideas, namely the unavoidable
lack of robustness (specific to tasks that fall into the social range of human abilities) and the
fact that we are concept-driven, a way to approach the design of social robots is outlined in
the next chapter.
Note that the ideas developed in this chapter constitute a coherent explanation of the
well-known fact that certain tasks that are trivial for us are hard for computers/robots and
in the set A we can obviously expect low error in the future. However, such reasoning is essentially equivalent
to saying that the more (labelled) samples we know of the domain the more reliable the error figure measured
on those samples. But knowing many samples of the domain is actually equivalent to knowing something about
the solution function, only that it would be knowledge by enumeration, instead of by comprehension. Having
little knowledge (by comprehension) of the solution is just equivalent to knowing only a very small part of the
possible samples in the domain.
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