Abstract :
In this article, we propose a graph-model based technique, identified as demonstration graph, to construct and coordinate both behavioral and cognitive models automatically for IHCs to accomplish complex tasks in a simple, universal way. Our technique is inspired by the insight from psychology, neuroscience, and human ethology that humans´ decision making largely relies on their past, similar experiences.5,6 Our work is further supported by Alan Turing, who believed that building an intelligent system necessitates imitating human mental processing.7 Thus, we use the Learning-from-Demonstrations (LfD) method,8 borrowed from the robotics domain, to make a character mimic successful human demonstrations to accomplish a well-defined task.
Keywords :
behavioural sciences computing; cognitive systems; decision making; graph theory; learning (artificial intelligence); psychology; IHC; LfD method; character behavior planning; cognitive models; demonstration graph; graph-model based technique; human decision making; human demonstrations; human ethology; human mental processing; intelligent system; interactive humanlike character; learning-from-demonstrations method; neuroscience; psychology; robotics domain; virtual 3D space; visual simulation; Animation; Behavioral science; Decision making; Human factors; Learning systems; Real-time systems; Three dimensional displays; Trajectory; 3D imaging; Learning-from-Demonstrations; LfD; behavioral modeling; character animation; cognitive modeling; intelligent character; multimedia; planning;