Using and Refining Simplifications: Explanation-Based Learning of Plans in Intractable Domains
Proceedings of the Eleventh International Joint Conference on Artificial Intelligence. Machine Learning. Pages 590-594.
Abstract
This paper describes an explanation-based approach to learning plans despite a computationally intractable domain theory. In this approach, the system learns an initial plan using limited inference. In order to detect plans in which the limited inference causes a faulty plan the system monitors goal achievement in plan execution. When a plan unexpectedly fails to achieve a goal (or unexpectedly achieves the goal) a refinement process is trigg ered in which the system constructs an explanation for the expectation violation. This explanation is then used to refine the plan. By using expectation failures to guide search the learner avoids a computationally intractable exhaustive search involved in constructing a complete proof of the plan . This approach has the theoretical property of convergence upon a sound plan .
@inproceedings{ijcai1989p94,
title = {Using and Refining Simplifications: Explanation-Based Learning of Plans in Intractable Domains},
author = {Chien, Steve A.},
booktitle = {Proceedings of the Eleventh International Joint Conference on
Artificial Intelligence, {IJCAI-89}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {N. S. Sridharan},
pages = {590--594},
year = {1989},
month = {8},
note = {Machine Learning},
url = {https://ijcai.org/proceedings/1989/94},
}
TY - CPAPER TI - Using and Refining Simplifications: Explanation-Based Learning of Plans in Intractable Domains AU - Chien, Steve A. T2 - Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, IJCAI-89 PB - International Joint Conferences on Artificial Intelligence PY - 1989 SP - 590 EP - 594 UR - https://ijcai.org/proceedings/1989/94 ER -Copied
Machine-readable versions of this paper, for tools and AI agents:
