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derson et al., 1997), focus on ‘higher’ cognitive func-
tions without considering the basis for these higher
functions, i.e. generation and valuation of goals. Only
a few cognitive architectures, such as MicroPsi (Bach,
2011) and CLARION (Sun, 2007), also focus on mo-
tivational aspects. However, these approaches do not
consider a generative and embodied approach. For
instance, MicroPsi distinguishes physiological, cog-
nitive and social needs, which are all hard-wired. In
the embodied approach of the ARS model all needs
are grounded in physiological needs. Based on so-
cial rules and the agent’s memories of how to sat-
isfy physiological needs in the short and long term,
they are transformed into complex motivations and
goals. CLARION in fact considers the derivation of
‘secondary’ drives from ‘primary’ drives, but uses the
drive-concept more in terms of a behavioristic ap-
proach, since it is based on Hull’s concept of drives
(Hull, 1951). In contrast to the concept of drives in
ARS, the drives’ strength in CLARION is determined
by an internal deficit and an external stimulus (e.g.
food in case of hunger). In this regard, the ARS multi-
level approach enables a more flexible motivational
system: generally speaking, a first level of motivation
and valuation considers only how to satisfy its motiva-
tion best according to the agent’s memories and with-
out consideration of the external world. After further
levels of motivation and valuation, it is only in the
ARS secondary process that the reality imposed by
the external world is considered.
Emotions are a central aspect of motivations and
valuation; they can be seen in general terms as a rep-
resentation of an agent’s internal state (emotions as
embodied information of valuation and importance)
(S. C. Marsella and Petta, 2010). Recently, various
stand-alone computational models of emotion have
been developed, i.e. they are usually not integrated
into a full-fledged cognitive architecture. Due to its
focus on the connection between emotion and cogni-
tion (S. C. Marsella and Petta, 2010), cognitive ap-
praisal theory is currently the dominant theory for
computer models of emotions. In this theory, emo-
tions emerge from the appraisal of external events
and situations under the consideration of the agent’s
beliefs, desires and intentions. The result of such
appraisal is the triggering of cognitive responses, in
particular coping strategies (e.g. planning, procrasti-
nation) (S. C. Marsella and Petta, 2010). Appraisal
theories focus on determining a sufficient set of ap-
praisal criteria to explain the elicitation and differen-
tiation of emotions. A widely used model of cog-
nitive appraisal theory is the OCC model (Ortony
et al., 1990) (e.g. used in EMA (Marsella and Gratch,
2009)), with appraisal criteria such as unexpected-
ness, level of appeal and desirability. In appraisal
theory, emotions are only elicited by evaluations of
external events and hence only considered for interac-
tion purposes. This is a major difference to the ARS
model, where the generation of emotions is influenced
by external events on the one hand and based on the
agent’s drives on the other hand. In particular, this
means the agent may be in an emotional state even
without consideration of the external world. Another
category of emotion theories follows a dimensional
approach and models emotions not as discrete enti-
ties but as points in a continuous dimensional space
(S. C. Marsella and Petta, 2010). A typical example
for this is the three-dimensional PAD model (Mehra-
bian and Russell, 1974), with pleasure (a measure of
valence), arousal (indicating the level of affective ac-
tivation) and dominance (a measure of power or con-
trol) as dimensions.
3 ARS APPROACH
The leitmotif of the ARS approach (Dietrich et al.,
2009) is to model the functions behind the desired ca-
pabilities that generate behavior instead of simply de-
scribing behavior. This complies with the generative
and broad approach of Artificial General Intelligence
(AGI).
In the ARS project, the human cognitive architec-
ture (i.e. the brain) is considered as an information
processing system that stores, manipulates and trans-
fers information. Following the standard approach in
computer technology, it is described in a top-down
design process using a layered model, starting with
three functional layers. The first layer, the neurons,
can be described as hardware under consideration of
the laws of physics. The next layer is called the neu-
rosymbolic layer, which handles the symbolization of
the neural layer. The third layer represents the psy-
chic layer, which is described in functional terms on
an algorithmic level. Following a monistic view, psy-
che and brain are of course the same, with only differ-
ent models being used in their respective descriptions.
Since only a functional description is relevant for ar-
tificial systems (but not how these functions are im-
plemented), the ARS project focuses on the descrip-
tion of the psychic apparatus. As mentioned in the in-
troduction, the second topographical model of Freud
(Freud, 1915) was chosen as a general framework,
which uses the abstract functions Id, Ego and Super-
Ego to describe the human psychic apparatus. The Id
represents drives, which are in effect bodily demands
coming from internal sensors, the Super-ego repre-
sents internalized moral demands and the Ego me-
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