Realizing believable agents: an integration of the 'author-based' and the 'model-based' approaches
Title:
Realizing believable agents: an integration of the 'author-based' and the 'model-based' approaches
Author:
Paola Rizzo
Appeared in:
AI communications
Paging:
Volume 13 (2001) nr. 3 pages 145-168
Year:
2001-04-01
Contents:
A major research problem regarding believable agents is how to develop and execute their behavioral libraries. This work identifies two different approaches: the 'author-based' one, depending on the designer's ability to hand-code each agent's behavioral features, and the 'model-based' one, grounded on a model which, starting from a set of primitives provided by the designer, automatically generates the agents' typical actions. This paper proposes to integrate the two methods by means of a two-phase/two-step strategy, that partially relieves the designer of the burden of hand-coding all the behavioral libraries, while still allowing a good control over the characters' performance, and enabling the runtime creation and storage of new behaviors. A case study concretely illustrates how such strategy is implemented by means of a hybrid planning architecture, coupled with a goal-based model of personality, in order to realize characters that interact with the user according to their personalities.
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