ABL is a programming language explicitly designed to support programming idioms for the creation of reactive, believable agents (Mateas and Stern, 2004). ABL has been successfully used to author the central characters Trip and Grace for the interactive drama Facade (Mateas and Stern, 2003). The ABL compiler is written in Java and targets Java; the generated Java code is supported by the ABL runtime system.
Published in Chapter:
Emotional Memory and Adaptive Personalities
Anthony G. Francis Jr. (Google, USA), Manish Mehta (Georgia Institute of Technology, USA), and Ashwin Ram (Georgia Institute of Technology, USA)
Copyright: © 2009
|Pages: 22
DOI: 10.4018/978-1-60566-354-8.ch020
Abstract
Believable agents designed for long-term interaction with human users need to adapt to them in a way which appears emotionally plausible while maintaining a consistent personality. For short-term interactions in restricted environments, scripting and state machine techniques can create agents with emotion and personality, but these methods are labor intensive, hard to extend, and brittle in new environments. Fortunately, research in memory, emotion and personality in humans and animals points to a solution to this problem. Emotions focus an animal’s attention on things it needs to care about, and strong emotions trigger enhanced formation of memory, enabling the animal to adapt its emotional response to the objects and situations in its environment. In humans this process becomes reflective: emotional stress or frustration can trigger re-evaluating past behavior with respect to personal standards, which in turn can lead to setting new strategies or goals. To aid the authoring of adaptive agents, we present an artificial intelligence model inspired by these psychological results in which an emotion model triggers case-based emotional preference learning and behavioral adaptation guided by personality models. Our tests of this model on robot pets and embodied characters show that emotional adaptation can extend the range and increase the behavioral sophistication of an agent without the need for authoring additional hand-crafted behaviors.