DFKI-LT - Learnable Controllers for Adaptive Dialogue Processing Management
Learnable Controllers for Adaptive Dialogue Processing Management
2 Proceedings of the AAAI 2010 Fall Symposium "Dialogue with Robots", Arlington, VA, USA, AAAI, 11/2010
The paper focuses on how a model could be learnt for determining at runtime how much of spoken input needs to be understood, and what configuration of processes can be expected to yield that result. Typically, a dialogue system applies a fixed configuration of shallow and deep forms of processing to its input. The configuration tries to balance robustness with depth of understanding, creating a system that always tries to understand as well as it can. The paper adopts a different view, assuming that what needs to be understood can vary per context. To facilitate this any-depth processing, the paper proposes an approach based on learnable controllers. The paper illustrates the main ideas of the approach on examples from a robot acquiring situated dialogue competence, and a robot working with users on a task.
Files: BibTeX, kruijff+kriegerfs2010.pdf