Abstract

This article addresses the problem of scalable optimization for spatially-aware dialogue systems. These kinds of systems must perceive, reason, and act about the spatial environment where they are embedded. We formulate the problem in terms of Semi-Markov Decision Processes and propose a hierarchical reinforcement learning approach to optimize subbehaviors rather than full behaviors. Because of the vast number of policies that are required to control the interaction in a dynamic environment (e.g., a dialogue system assisting a user to navigate in a building from one location to another), our learning approach is based on two stages: (a) the first stage learns low-level behavior, in advance; and (b) the second stage learns high-level behavior, in real time. For such a purpose we extend an existing algorithm in the literature of reinforcement learning in order to support reusable policies and therefore to perform fast learning. We argue that our learning approach makes the problem feasible, and we report on a novel reinforcement learning dialogue system that performs a joint optimization between dialogue and spatial behaviors. Our experiments, using simulated and real environments, are based on a text-based dialogue system for indoor navigation. Experimental results in a realistic environment reported an overall user satisfaction result of 89%, which suggests that our proposed approach is attractive for its application in real interactions as it combines fast learning with adaptive and reasonable behavior.