Learning Situation-Dependent Rules: Improving Task Planning for an Incompletely Modelled Domain

Karen Zita Haigh and Manuela Veloso
1999 AAAI Spring Symposium on Search Techniques for Problem Solving under Uncertainty and Incomplete Information, March, 1999.


Download
  • Adobe portable document format (pdf) (188KB)
Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract
Most real world environments are hard to model completely and correctly, especially to model the dynamics of the environment. In this paper we present our work to improve a domain model through learning from execution, thereby improving a task planner's performance. Our system collects execution traces from the robot, and automatically extracts relevant information to improve the domain model. We introduce the concept of {\em situation-dependent rules}, where situational features are used to identify the conditions that affect action achievability. The system then converts this execution knowledge into a symbolic representation that the planner can use to generate plans appropriate for given situations.

Notes

Text Reference
Karen Zita Haigh and Manuela Veloso, "Learning Situation-Dependent Rules: Improving Task Planning for an Incompletely Modelled Domain," 1999 AAAI Spring Symposium on Search Techniques for Problem Solving under Uncertainty and Incomplete Information, March, 1999.

BibTeX Reference
@inproceedings{Veloso_1999_2909,
   author = "Karen Zita Haigh and Manuela Veloso",
   title = "Learning Situation-Dependent Rules: Improving Task Planning for an Incompletely Modelled Domain",
   booktitle = "1999 AAAI Spring Symposium on Search Techniques for Problem Solving under Uncertainty and Incomplete Information",
   month = "March",
   year = "1999",
}