REPRESENTATIONS AND REASONING FOR GOAL-ORIENTED CONVERSATIONS

摘要

The use of a goal-understanding abstraction hierarchy in conjunction with Bayesian inference, decision-theoretic analyses of conversational and observational actions is disclosed to provide machinery for incremental refinement of an understanding about a user's goals through conversation with users. A computer-implemented method receives multiple classes of information regarding a user goal including visual and linguistic clues at a specific level of the abstraction hierarchy, to assess the goal. The method then determines with a value-of-information analysis the utility of acquiring additional information via making additional observations or by explicity querying the user versus making a decision to change the level of precision of the analysis of a user's goals. Throughout the analysis, a probability distribution is inferred about the goals of a user. This probability distribution is used in conjunction with a representation of utility of different outcomes to identify informational and navigational actions with the greatest expected utility.