Accession Number : ADA295449
Title : Behavior-Based Language Generation for Believable Agents,
Corporate Author : CARNEGIE-MELLON UNIV PITTSBURGH PA DEPT OF MATHEMATICS
Personal Author(s) : Loyall, A. B. ; Bates, Joseph
PDF Url : ADA295449
Report Date : MAR 1995
Pagination or Media Count : 18
Abstract : 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 extensions 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 believability, 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 extensions may interest researchers seeking to generate language in other action architectures.
Descriptors : *COMPUTATIONAL LINGUISTICS, *NATURAL LANGUAGE, REAL TIME, COMPUTER ARCHITECTURE, ARTIFICIAL INTELLIGENCE, PERCEPTION.
Subject Categories : Linguistics
Distribution Statement : APPROVED FOR PUBLIC RELEASE