  We observe a change in the way users access information, that is, the rise of
conversational information access (CIA) agents. However, the automatic
evaluation of these agents remains an open challenge. Moreover, the training of
CIA agents is cumbersome as it mostly relies on conversational corpora, expert
knowledge, and reinforcement learning. User simulation has been identified as a
promising solution to tackle automatic evaluation and has been previously used
in reinforcement learning. In this research, we investigate how user simulation
can be leveraged in the context of CIA. We organize the work in three parts. We
begin with the identification of requirements for user simulators for training
and evaluating CIA agents and compare existing types of simulator regarding
these. Then, we plan to combine these different types of simulators into a new
hybrid simulator. Finally, we aim to extend simulators to handle more complex
information seeking scenarios.
