Towards Online Planning for Dialogue Management with Rich Domain Knowledge

Most approaches to dialogue management have so far concentrated on offline optimisation techniques, where a dialogue policy is precomputed for all possible situations and then plugged into the dialogue system. This development strategy has however some limitations in terms of domain scalability and adaptivity, since these policies are essentially static and cannot readily accommodate runtime changes in the environment or task dynamics. In this paper, we follow an alternative approach based on online planning. To ensure that the planning algorithm remains tractable over longer horizons, the presented method relies on probabilistic models expressed via probabilistic rules that capture the internal structure of the domain using high-level representations. We describe in this paper the generic planning algorithm, ongoing implementation efforts and directions for future work.