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The Motivation System 117

the net [A, V, S] lies within the arousal, valence, and stance boundaries that define the
corresponding emotion region shown in figure 8.3. This value is scaled with respect to
the size of the region so as to not favor the activation of some processes over others in
the arbitration phase. The contribution of each dimension to each elicitor is computed
individually. If any one of the dimensions is not represented, then the activation level is
set to zero. Otherwise, the A, V, and S contributions are summed together to arrive at
the activation level of the elicitor. This activation level is passed on to the corresponding
emotion process in the arbitration phase.
There are many different processes that contribute to the overall affective state. Influences
are sent by drives, the active behavior, and releasers. Several different schemes for com-
puting the net contribution to a given emotion process were tried, but this one has the nicest
properties. In an earlier version, all the incoming contributions were simply averaged. This
tended to “smooth” the net affective state to an unacceptable degree. For instance, if the
robot’s fatigue-drive is high (biasing a low arousal state) and a threatening toy appears
(contributing to a strong negative valence and high arousal), the averaging technique could
result in a slightly negative valence and neutral arousal. This is insufficient to evoke fear
and an escape response when the robot should protect itself. As an alternative, we could
hard-wire certain releasers directly to emotion processes. It is not clear, however, how this
approach supports the influence of drives and behaviors, whose affective contributions
change as a function of time. For instance, a given drive contributes to fear, sorrow,
or interest processes depending on its current activation regime. The current approach
balances the constraints of having certain releasers contribute heavily and directly to the
appropriate emotive response, while accommodating those influences that contribute to dif-
ferent emotions as a function of time. The end result also has nice properties for generating
facial expressions that reflect this assessment process in a rich way. This is important for
social interaction as originally argued by Darwin. This expressive benefit is discussed in
further detail in chapter 10.

Emotion Activation
Next, the activation level of each emotion process is computed. There is a process defined
for each emotion listed in table 8.1: joy, anger, disgust, fear, sorrow, surprise,
interest, boredom, and calm.
Numerically, the activation level A emotion of each emotion process can range between
[0, A max ] where A max is an integer value determined empirically. Although these pro-
emotion emotion
cesses are always active, their intensity must exceed a threshold level before they are
expressed externally. The activation of each process is computed by the equation:

A emotion = (E emotion + B emotion + P emotion ) − δ t