Spoken language interaction with model uncertainty: an adaptive human-robot interaction system

Spoken language is one of the most intuitive forms of interaction between humans and agents. Unfortunately, agents that interact with people using natural language often experience communication errors and do not correctly understand the user's intentions. Recent systems have successfully used probabilistic models of speech, language, and user behavior to generate robust dialog performance in the presence of noisy speech recognition and ambiguous language choices. However, decisions made using these probabilistic models are still prone to errors due to the complexity of maintaining a complete model of human intentions. In this paper, we describe a decision-theoretic model for human-robot interaction using natural language. Our algorithm is based on the Partially Observable Markov Decision Process (POMDP), which allows agents to choose actions that are robust not only to uncertainty from noisy or ambiguous speech recognition but also unknown user models. Like most dialog systems, ...

• Type: Journal
• Year: 2008
• Where: CONNECTION
• Authors: Finale Doshi, Nicholas Roy
• Keywords: CONNECTION 2008, Natural Language, Partially Observable Markov Decision Process, Probabilistic Models