Attention, intentions, and the structure of discourse
Computational Linguistics
How do users know what to say?
interactions
ELIZA—a computer program for the study of natural language communication between man and machine
Communications of the ACM
Designing and Evaluating an Adaptive Spoken Dialogue System
User Modeling and User-Adapted Interaction
Dialogue Modelling for a Conversational Agent
AI '01 Proceedings of the 14th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Agent-Based Adaptive Interaction and Dialogue Management Architecture for Speech Applications
TSD '01 Proceedings of the 4th International Conference on Text, Speech and Dialogue
Speech technology on trial: Experiences from the August system
Natural Language Engineering
Towards developing general models of usability with PARADISE
Natural Language Engineering
MIMIC: an adaptive mixed initiative spoken dialogue system for information queries
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Evaluating response strategies in a Web-based spoken dialogue agent
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
An agent-based approach to dialogue management in personal assistants
Proceedings of the 10th international conference on Intelligent user interfaces
An architecture and applications for speech-based accessibility systems
IBM Systems Journal
The RavenClaw dialog management framework: Architecture and systems
Computer Speech and Language
Optimizing dialogue management with reinforcement learning: experiments with the NJFun system
Journal of Artificial Intelligence Research
| Hi-index | 12.06 |
In this paper, we focus on an agent-based architecture for task-oriented dialogue management. We present our deliberation process based on optimizing the length of a dialogue in terms of its turns needed to accomplish a task. As innovative features, the manager accommodates a ''lounge strategy'' for passing initiative back to the user, and a two-layered structure for dialogue context representation. We show that applying the lounge strategy during a dialogue leads to improving the information exchange rate by approximately 5% compared to ''common'' dialogue strategies alternative. Furthermore, we present two of our dialogues and analyze them with respect to the agent description. At the end of the paper, we suggest future extensions and modifications to the architecture, and conclude.