Generating efficient belief models for task-oriented dialogues

Abstract

We have shown that belief modelling for dialogue can be simplified if the assumption is made that the participants are cooperating, i.e., they are not committed to any goals requiring deception. In such domains, there is no need to maintain individual representations of deeply nested beliefs; instead, three specific types of belief can be used to summarize all the states of nested belief that can exist about a domain entity. Here, we set out to design a ``compiler'' for belief models. This system will accept as input a description of agents' interactions with a task domain expressed in a fully-expressive belief logic with non-monotonic and temporal extensions. It generates an operational belief model for use in that domain, sufficient for the requirements of cooperative dialogue, including the negotiation of complex domain plans. The compiled model incorporates the belief simplification mentioned above, and also uses a simplified temporal logic of belief based on the restricted circumstances under which beliefs can change. We shall review the motivation for creating such a system, and introduce a general procedure for taking a logical specification for a domain and procesing it into an operational model. We shall then discuss the specific changes that are made during this procedure for limiting the level of abstraction at which the concepts of belief nesting, default reasoning and time are expressed. Finally we shall go through a worked example relating to the Map Task, a simple cooperative problem-solving exercise.