Jason Williams Title: Spoken dialog systems as an application of POMDPs Abstract: Spoken dialog systems present a classic example of planning under uncertainty. Speech recognition errors are ubiquitous and impossible to detect reliably, so the state of the conversation can never be known with certainty. Despite this, the system must choose actions to make progress to a long term goal. As such, POMDPs present an attractive approach to building spoken dialog systems. Initial work on "toy" dialog systems validated the benefits of the POMDP approach; however, it also found that straightforward application of POMDPs could not scale to real-world problems. Subsequent work by a number of research teams has scaled up planning and belief monitoring, incorporated high-fidelity user simulations, and married commercial development practices with automatic optimization. Today, statistical dialog systems are now being fielded by research labs for public use. Yet in achieving this, researchers have gradually abandoned classical POMDP planning in favor of reinforcement learning. Examining the reasons for this shift suggest several important areas of future POMDP research.