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Behavior-based language generation for believable agents

By A. Bryan Loyall and Joseph Bates

Abstract

bryan, loyal l @ cs.cmu.edu, j oseph.bates @ cs.cmu.edu We are studying how to create believable agents that perform actions and use natural language in interactive, animated, real-time worlds. We have extended Hap, our behavior-based architecture for believable non-linguistic agents, to better support natural language text generation. These ex-tensions allow us to tightly integrate generation with other aspects of the agent, including action, perception, inference and emotion. We describe our approach, and show how it leads to agents with properties we believe important for be-lievability, such as: using language and action together to accomplish communication goals; using perception to help make linguistic choices; varying generated text according to emotional state; and issuing the text in real-time with pauses, restarts and other breakdowns visible. Besides being useful in constructing believable agents, we feel these exten-sions may interest researchers seeking to generate language in other action architectures

Year: 1995
DOI identifier: 10.21236/ada295449
OAI identifier: oai:CiteSeerX.psu:10.1.1.486.7701
Provided by: CiteSeerX
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