Task Models, Intentions, and Agent Conversation Policies

It is possible to define conversation policies, such as communication or dialogue protocols, that are based strictly on what messages and, respectively, what performatives may follow each other. While such an approach has many practical applications, such protocols support only "local coherence" in a conversation. Lengthy message exchanges require some infrastructure to lend them "global coherence." Recognition of agent intentions about the joint task is essential for this global coherence, but there are further mechanisms needed to ensure that both local and global coherence are jointly maintained. This paper presents a general yet practical approach to designing, managing, and engineering agents that can do simple run-time intention recognition without creating complex multi-state protocols. In this approach we promote developing abstract task models and designing conversation policies in terms of such models. An implemented agent assistant based on these ideas is briefly described.