From Interaction Data to Plan Libraries: A Clustering Approach
Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence. Planning and Scheduling. Pages 962-966.
Abstract
Plan libraries are the most important knowledge source of many plan recognition systems. The plan decom positions they contain provide information about how a plan has to be executed to actually achieve its associated goals and be recognized by the system. This paper presents an approach to the automatic acquisition of plan decompositions from sample action sequences. In parti cular a clustering algorithm is introduced that allows groups of "similar" sequen ce s to be discovered and used for the generat ion of plan libraries. Empirical tests indicate that these libraries can indeed be successfully used for plan recognition purposes.
@inproceedings{ijcai1999p137,
title = {From Interaction Data to Plan Libraries: A Clustering Approach},
author = {Bauer, Mathias},
booktitle = {Proceedings of the Sixteenth International Joint Conference on
Artificial Intelligence, {IJCAI-99}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Thomas Dean},
pages = {962--966},
year = {1999},
note = {Planning and Scheduling},
url = {https://ijcai.org/proceedings/1999/137},
}
TY - CPAPER TI - From Interaction Data to Plan Libraries: A Clustering Approach AU - Bauer, Mathias T2 - Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, IJCAI-99 PB - International Joint Conferences on Artificial Intelligence PY - 1999 SP - 962 EP - 966 UR - https://ijcai.org/proceedings/1999/137 ER -Copied
Machine-readable versions of this paper, for tools and AI agents:
