Approaches that apply programming by demonstration (PbD) to automatically generate the behaviors of virtual characters have been actively studied. One of PbD directly delivers the knowledge of a predecessor to the virtual character. Therefore, a virtual character learns the behaviors to be executed by observing the behaviors of a predecessor. All consecutive actions are derived from the actions collected as behaviors. The behaviors to be executed are selected from defined behaviors using the Maximin Selection algorithm. However, these approaches collect a large amount of data in real time. Therefore, the amount of data significantly increases, and their analysis becomes difficult. This paper proposes a toolkit that employs PbD to automatically generate the behaviors of virtual characters based on those of a predecessor. Furthermore, an approach to manage and analyze the collected data is described. On the basis of the results of an experiment, it was verified that the proposed toolkit could generate a script of the behaviors of virtual characters for driving in a car simulation. © 2011 Springer-Verlag.
CITATION STYLE
Sung, Y., & Cho, K. (2011). An automatic behavior toolkit for a virtual character. In Communications in Computer and Information Science (Vol. 262 CCIS, pp. 146–154). https://doi.org/10.1007/978-3-642-27204-2_17
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