Who Needs Emotions? The Brain Meets the Robot

376 conclusions
Consider a robot that has a core set of basic functions, each with appropriate
perceptual schemas, F 1, F 2 ... F n (generalizing the four Fs!), each of
which has access to more or less separate motor controllers, M 1, M 2 ... M n,
though these may share some motor subschemas, as in the use of orienting
and locomotion for prey capture and predator avoidance in the frog. Each F j
evaluates the current state to come up with an urgency level for activating
its motor schema M j, as well as determining appropriate motor parameters
(it is not enough just to snap, but the frog must snap at the fly). Under basic
operating conditions, a winner-take-all or similar process can adjudicate between
these processes (does the frog snap at the fly or escape the predator?).
We might want to say, then, that a motivational system is a state-evaluation
processes that can adjust the relative weighting of the different functions,
raising the urgency level for one system while lowering the motivation system
for others, so that a stimulus that might have activated M k in one context
will now instead activate M i.
What if, as is likely, the set of tasks is not a small set of survival tasks but
indeed a very large set? It may help to recall (Watts, 2003) that the procurement
phase in animal behavior is individualized for the particular situation
and can be quite complex, whereas the subsequent consummatory phase
involves more stereotypic movements. What I take from this is not the idea
of organizing a variety of behaviors with respect to the consummatory phase
they serve but, rather, the idea that action selection may well involve grouping
a large set of actions into a small number of groups, each containing many
actions or tasks. In other words, we consider the modes of the S-RETIC model
as abstract groups of tasks rather than as related to biological drives like the
four Fs. I consider the case where there are m strategies which can be grouped
into n groups (with n much less than m) such that it is in general more efficient,
when faced with a problem, to first select an appropriate group of
strategies and then to select a strategy from within that group. The catch, of
course, is in the caveat “in general.” There may be cases in which rapid commitment
to one group of strategies may preclude finding the most appropriate
strategy—possibly at times with disastrous consequences. Effective
robot design would thus have to balance this fast commitment process against
more subtle evaluative process that can check the suitability of a chosen strategy
before committing to it completely. We might then liken motivation to
biases which favor one strategy group over another and emotion to the way
in which these biases interact with more subtle computations. On this abstract
viewpoint, the “passionate robot” is not one which loses its temper in
the human-like fashion of the computer tutor imagined earlier but rather
one in which biases favoring rapid commitment to one strategy group overwhelm
more cautious analysis of the suitability of strategies selected from
that group for the task at hand.