A Plan-Based Dialog System with Probabilistic Inferences

In this paper, we present a dialog system that extends the planbased approach with two features. Instead of Boolean inference, we include into the system the probabilistic measures from the front-end speech and language processes. As a result, rules can be activated and facts gathered based on statistical confidence measures. We also introduce the notion of entity types to classify the rules and facts. The entity types, derived from the schema of the knowledge base, assist the semantic evaluation process by indicating which rules and facts are interoperable. The semantic evaluation and dialog planning can therefore be better insulated among tasks, and be encapsulated into reusable components. To assess the feasibility and discover the areas for improvements for this framework, we launched the project DR. WHO, in which we strive to develop a speech interface for a personal information management application. We describe the current progress in the discourse area in this paper.