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when it knows that in the future it will need those items, even if in the state, it does not
need those items to fulfill the immediate drives. This is especially true for a meticulous
personality, because it tends to spend more time to carry out actions. Thus, meticulous
agents take less time to execute plans than careless agents.
5
Related Work
During the last years, several emotion-oriented systems have been developed, that nor-
mally follow Frijda's theory about emotions [18]. This theory is based on the hypothesis
that emotions are the tendency of an individual to adopt a specific behavior according
to its needs. Emotions also cover the interaction of the individual with the environment.
For instance, individuals try to move away objects that put in danger their survival,
while they approach objects that cater for their needs [6].
Examples of previous work on computational models of emotions is the work of
Ca namero [7,8] that proposes a homeostatic approach to the motivations model. She
creates a self-regulatory system, very close to natural homeostasis, that connects each
motivation to a physiological variable, which is controlled within a given range. When
the value of that variable differs from the ideal one, an error signal proportional to
the deviation, called drive, is sent, and activates some control mechanism that adjusts
the value in the right direction. There are other architectures based on drives, as the
Dorner's PSI architecture used by Bach and Vuine [3] and also by Lim [23], that offer
a set of drives of different type, as certainty, competence or affiliation.
Most of these works on emotional agents are based on reactive behaviors. When a
drive is detected, it triggers a reactive component that tries to compensate its deviation,
taking into account only the following one or two actions. Thus, there is no inference
being done on medium-long term goals and the influence of emotions on how to achieve
those goals. Our model borrows the concepts of motivations and drives to represent
basic needs, but it uses automated planning for providing long term deliberation.
Regarding deliberative models, there are some works on emotions based on plan-
ning, but mainly oriented to storytelling. Examples are emergent narrative in
F
EAR
N
OT
! [2] and the interactive storytelling of Madame Bovary on the Holodeck [9].
The work of Gratch and coauthors [20,21] shows a relevant application of emotional
models to different research areas in artificial intelligence and autonomous agents de-
sign, endowing them with an ability to think and engage in socio-emotional interactions
with human users. Other models, as Rizzo's works [25], combine the use of emotions
and personality to assign preferences to the goals of a planning domain model, but the
changes in the emotional state happen in another module. Thus, they are not really used
in the reasoning process. A similar integration of a deliberative and a reactive model is
the one in [5] where the emotions reasoning is performed again by the reactive compo-
nent. Opposite to all these approaches, there are no hard constraints on our model. All
our agents can perform all actions, but they prefer (soft constraints) the ones that better
suit their preferences, personality and current emotional state.
