Reference:
El-Nasr, M.S., Yen, J., and Ioerger, T.R. (2000). FLAME: Fuzzy Logic Adaptive
Model of Emotions. Autonomous Agents and Multi-Agent Systems,
3(3):219-257.
Abstract:
Emotions are an important aspect of human intelligence and have been shown to
play a significant role in the human decision-making process. Researchers in
areas such as cognitive science, philosophy, and artificial intelligence have
proposed a variety of models of emotions. Most of the previous models focus
on an agent's reactive behavior, for which they often generate emotions
according to static rules or pre-determined domain knowledge. However,
throughout the history of research on emotions, memory and experience have
been emphasized to have a major influence on the emotional process. In this
paper, we propose a new computational model of emotions that can be
incorporated into intelligent agents and other complex, interactive programs.
The model uses a fuzzy-logic representation to map events and observations to
emotional states. The model also includes several inductive learning
algorithms that learn patterns of events, associations among objects, and
expectations. We demonstrate empirically through a computer simulation of a
pet that the adaptive components of the model are crucual to users'
assessments of the believability of the agent's interactions.