DFKI-LT - Learning Dialogue Agents with Bayesian Relational State Representations
Learning Dialogue Agents with Bayesian Relational State Representations
Proceedings of the IJCAI Workshop on Knowledge and Reasoning in Practical Dialogue Systems (IJCAI-KRPDS),
A new approach is developed for representing the search space of reinforcement learning dialogue agents. This approach represents the state-action space of a reinforcement learning dialogue agent with relational representations for fast learning, and extends it with belief state variables for dialogue control under uncertainty. Our approach is evaluated, using simulation, on a spoken dialogue system for situated indoor wayfinding assistance. Experimental results showed rapid adaptation to an unknown speech recognizer, and more robust operation than without Bayesian-based states.
Files: BibTeX, hc-ijcai-krpds2011.pdf