Improving User Taught Task Models
Task models are essential components in many approaches to user modelling because they provide the context with which to interpret, predict, and respond to user behavior. The quality of such models is critical to their ability to support these functions. This paper describes work on improving task models that are automatically acquired from demonstration. Modifications to a standard planning algorithm are described and applied to an example learned task model, showing the utility of incorporating plan-based reasoning into task learning systems.
KeywordsUser Modelling Task Model Planning Domain Intelligent Tutoring System Order Constraint
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- 1.Litman, D.J., Silliman, S.: Itspoke: An intelligent tutoring spoken dialogue system. In: Proceedings of the Human Language Technology Conference: 4th Meeting of the North American Chapter of the Association for Computational Linguistics (HLT/NAACL), Boston, MA (2004)Google Scholar
- 3.Carberry, S.: Plan Recognition in Natural Language Dialogue. MIT Press, Cambridge (1990)Google Scholar
- 4.Jung, H., Allen, J., Chambers, N., Galescu, L., Swift, M., Taysom, W.: One-shot procedure learning from instruction and observation. In: Proceedings of the International FLAIRS Conference (FLAIRS-2006): Special Track on Natural Language and Knowledge Representation, AAAI Press, Stanford, California, USA (2006)Google Scholar
- 5.Lau, T., Domingos, P., Weld, D.: Learning programs from traces using version space algebra. In: Proceedings of the 2nd International Conference on Knowledge Capture (K-CAP) (2003)Google Scholar
- 6.Bauer, M.: Towards the automatic acquisition of plan libraries. In: European Conference on Artificial Intelligence, pp. 484–488 ( 1998)Google Scholar