Conversations with an intelligent agent: Modeling and integrating patterns in communication among humans and agents.

摘要

There is an overwhelming variation in the ways an intelligent agent can rationalize communication with a conversational partner. This variation presents many incompatibilities that lead to the specialization of conversational capabilities. This has produced a plethora of models and ideas on how an intelligent agent should understand, interact with, and incorporate communication from a human conversational participant. This dissertation approaches this problem with the thesis that there exists a language between that of human natural language and the behavioral reasoning of an intelligent agent, and that this language is capable of not only unifying the various models used in literature, but also provides the foundation for a theoretical framework for an engineering methodology for building such models.; A theory of practical communication language is developed, including the introduction of the meaning-action concept, an expressive and powerful representation based on speech-act and dialogue-act theories, but extended with notions of behavioral operators as well as signatures that allow the operators to incorporate structured and well-defined concepts. An engineering methodology is presented for the construction of concepts, operators and rules that create the language and model of a specific domain, including methodology for the verification and validation of that language and model.; The resultant practical communication language methodology, based on the combination of rational communication and meaning-action concepts, will introduce several major enhancements to dialogue management. These enhancements include the use of meaning-action concepts as a shared medium and the introduction of a shared concept graph. This methodology will be used along with various dialogue models from human-human, human-agent and agent-agent communication to construct a task-oriented language and model called the task communication language framework. This framework is then implemented within an intelligent agent in a real-time resource management simulation.; A sample output listing from actual human interaction with that implementation is used to demonstrate that the resulting framework does indeed incorporate many of the disparate models of communication and their corresponding capabilities including command and control, information seeking, notification and bother, clarification, explanation, discussion, negotiation, mutual planning, interruption, feedback, adjustable autonomy and corrective dialogues.