Paper: Learning What To Talk About In Descriptive Games

ACL ID H05-1037
Title Learning What To Talk About In Descriptive Games
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2005
Authors

Text generation requires a planning mod- ule to select an object of discourse and its properties. This is specially hard in de- scriptive games, where a computer agent tries to describe some aspects of a game world. We propose to formalize this prob- lem as a Markov Decision Process, in which an optimal message policy can be defined and learned through simulation. Furthermore, we propose back-off poli- cies as a novel and effective technique to fight state dimensionality explosion in this framework.