Paper: Combining Reinformation Learning with Information-State Update Rules

ACL ID N07-1034
Title Combining Reinformation Learning with Information-State Update Rules
Venue Human Language Technologies
Session Main Conference
Year 2007
Authors

Reinforcement learning gives a way to learn under what circumstances to per- form which actions. However, this ap- proach lacks a formal framework for spec- ifying hand-crafted restrictions, for speci- fying the effects of the system actions, or for specifying the user simulation. The in- formation state approach, in contrast, al- lows system and user behavior to be spec- ified as update rules, with preconditions and effects. This approach can be used to specify complex dialogue behavior in a systematic way. We propose combining these two approaches, thus allowing a for- mal specification of the dialogue behavior, and allowing hand-crafted preconditions, with remaining ones determined via rein- forcement learning so as to minimize dia- logue cost.