Who Needs Emotions? The Brain Meets the Robot

eware the passionate robot 375
designed to perform, though certainly environmental considerations will have
their effect. The design of a Mars rover and a computer tutor must take into
account the difference between an environment of temperature extremes
and dust storms and an air-conditioned classroom. Nonetheless, my point
holds that whereas an animal’s set of tasks can be related more or (as in the
case of humans) somewhat less directly to the four Fs, in the case of robots,
there will be an immense variety of task sets, and these will constrain the
sensors, controllers, and effectors in a way which must be referred to the
task sets without any foundation in the biological imperatives that have
shaped the evolution of motivational and emotional systems for biological
creatures.
Another classic brain model is relevant here. Kilmer, McCulloch, and
Blum (1969) implemented McCulloch’s idea of extending observations on
the involvement of the reticular formation in switching the animal from sleep
to waking (Magoun, 1963) to the hypothesis that the reticular formation
was responsible for switching the overall mode of feeding or fleeing or whatever,
and then the rest of the brain, when set into this mode, could do the
more detailed computations. The data of Scheibel and Scheibel (1958) on
the dendritic trees of neurons of the reticular formation suggested the idea
of modeling the reticular formation as a stack of modules, each with a slightly
different selection of input but trying to decide to which mode to commit
the organism. They would communicate back and forth, competing and
cooperating until finally they reached a consensus on the basis of their diverse
input; that consensus would switch the mode of the organism. In this
framework, Kilmer, McCulloch, and Blum (1969) simulated a model, called
S-RETIC, of a modular system designed to compute modes in this cooperative
manner. Computer simulation showed that S-RETIC would converge
for every input in fewer than 25 cycles and that, once it had converged, it
would stay converged for the given input. When the inputs strongly indicate
one mode, the response is fast; but when the indication is weak, initial
conditions and circuit characteristics may strongly bias the final decision.
Within any mode of behavior many different acts are possible: if the cat
should flee, will it take the mouse or leave it, climb a tree or skirt it, jump a
creek or swim it? The notion is that a hierarchical structure that computes
modes and then acts within them, might in some sense be “better” (irrespective
of the particular structural basis ascribed to these functions) than one
that tries to determine successive acts directly.
For robot emotions, then, the issue is to what extent emotions may
contribute to or detract from the success of a “species” of robots in filling
their ecological niche. I thus suggest that an effort to describe robot emotions
requires us to analyze the tasks performed by the robot and the strategies
available to perform them.