Towards Dialogue Management in Relational Domains

Traditional approaches to dialogue management rely on a fixed, predefined set of state variables. For many application domains, the dialogue state is however best described in terms of a collection of varying number of entities and relations holding between them. These entities might correspond to objects, places or persons in the context of the interaction, or represent a set of tasks to perform. Such formalization of the state space is well-suited for many domains, but presents some challenges for the standard probabilistic models used in dialogue management, since these models are propositional in nature and thus unable to directly operate on such state representation. To address this issue, we present an alternative approach based on the use of expressive probabilistic rules that allow for limited forms of universal quantification. These rules take the form of structured mappings between input and output variables, and function as high-level templates for the probability and utility models integrated in the dialogue manager. We present in this abstract the general formalisation of this approach, focusing on the use of universal quantifiers to capture the relational structure of the domain.