We present a conversational learning agent that helps users navigate through complex and challenging spatial environments. The agent exhibits adaptive behaviour by learning spatiallyaware dialogue actions while the user carries out the navigation task. To this end, we use Hierarchical Reinforcement Learning with relational representations to efficiently optimize dialogue actions tightly-coupled with spatial ones, and Bayesian networks to model the user’s beliefs of the navigation environment. Since these beliefs are continuously changing, we induce the agent’s behaviour in real time. Experimental results, using simulation, are encouraging by showing efficient adaptation to the user’s navigation knowledge, specifically to the generated route and the intermediate locations to negotiate with the user. Index Terms: spoken dialogue systems, situated interaction, reinforcement learning, hierarchical control, Bayesian networks
['Heriberto Cuayáhuitl', 'Nina Dethlefs']
Optimizing Situated Dialogue Management in Unknown Environments.