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Planning clarification questions to resolve ambiguous references to objects

Our aim is to design robots that can have task directed conversations with humans about objects in a table top scene. One of the pre-requisites is that the robot is able to correctly identify the object to which another speaker refers. This is not trivial as human references to objects are often ambiguous, and rely on contextual information from the scene, the task, or the dialogue to resolve the reference. This paper describes work in progress on building a robot system able to plan the content of clarifying questions that when answered provide the robot with enough information to resolve ambiguous references. It describes an algorithm that models the degree of uncertainty about the binding of a referent using a probability distribution. We use the visual salience of the object as a way to generate the prior distribution over candidate objects, which we call the belief state. Then we generate action models, for the effects of various clarifying questions, on the fly. Finally we evaluate the mean reduction in the entropy of the resulting belief states. The method can be seen as a form of prior-posterior analysis, or as one step look ahead in an information state Markov decision process. We are currently implementing the algorithm in a robot and discuss the issues we have encountered to date.