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Carnegie Mellon Robotics Institute

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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",
}

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