  Prior approaches to realizing mixed-initiative human--computer referential
communication have adopted information-state or collaborative problem-solving
approaches. In this paper, we argue for a new approach, inspired by
coherence-based models of discourse such as SDRT \cite{asher-lascarides:2003a},
in which utterances attach to an evolving discourse structure and the
associated knowledge graph of speaker commitments serves as an interface to
real-world reasoning and conversational strategy. As first steps towards
implementing the approach, we describe a simple dialogue system in a
referential communication domain that accumulates constraints across discourse,
interprets them using a learned probabilistic model, and plans clarification
using reinforcement learning.
