Document-editing Assistants and Model-based Reinforcement Learning as a Path to Conversational AI
This work targets researchers and developers aiming to advance conversational AI by focusing on a tightly scoped domain and formal methods, but it is incremental as it builds on existing reinforcement learning approaches.
The paper argues that voice document editing is a promising domain for developing conversational AI assistants, and proposes using model-based reinforcement learning to enable efficient, goal-oriented interaction without explicit instruction.
Intelligent assistants that follow commands or answer simple questions, such as Siri and Google search, are among the most economically important applications of AI. Future conversational AI assistants promise even greater capabilities and a better user experience through a deeper understanding of the domain, the user, or the user's purposes. But what domain and what methods are best suited to researching and realizing this promise? In this article we argue for the domain of voice document editing and for the methods of model-based reinforcement learning. The primary advantages of voice document editing are that the domain is tightly scoped and that it provides something for the conversation to be about (the document) that is delimited and fully accessible to the intelligent assistant. The advantages of reinforcement learning in general are that its methods are designed to learn from interaction without explicit instruction and that it formalizes the purposes of the assistant. Model-based reinforcement learning is needed in order to genuinely understand the domain of discourse and thereby work efficiently with the user to achieve their goals. Together, voice document editing and model-based reinforcement learning comprise a promising research direction for achieving conversational AI.