Using planning techniques in intelligent tutoring systems
International Journal of Man-Machine Studies
Generating descriptions that exploit a user's domain knowledge
Current research in natural language generation
Planning English Sentences
Generating referring expressions in a domain of objects and processes (language representation)
Generating referring expressions in a domain of objects and processes (language representation)
An Analysis of Initiative Selection in CollaborativeTask-Oriented Discourse
User Modeling and User-Adapted Interaction
IEEE Intelligent Systems
Mechanisms for mixed-initiative human-computer collaborative discourse
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
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We hypothesise that agents who engage in task oriented dialogue usually try to complete the task with the least effort which will produce a satisfactory solution. Our analysis of a corpus of map navigation task dialogues shows that there are a number of different aspects of dialogue for which agents can choose either to expend extra effort when they produce their initial utterances, or to take the risk that they will have to recover from a failure in the dialogue. Some of these decisions and the strategies which agents use to recover from failures due to high risk choices are simulated in the JAM system. The human agents of the corpus purposely risk failure because this is generally the most efficient behaviour. Incorporating the same behaviour in the JAM system produces dialogue with more "natural" structure than that of traditional dialogue systems.