Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Planning for conjunctive goals
Artificial Intelligence
Practical planning: extending the classical AI planning paradigm
Practical planning: extending the classical AI planning paradigm
Proceedings of the sixth international workshop on Machine learning
Explanation-Based Generalization: A Unifying View
Machine Learning
Explanation-Based Learning: An Alternative View
Machine Learning
Reasoning about change: time and causation from the standpoint of artificial intelligence
Reasoning about change: time and causation from the standpoint of artificial intelligence
STRIPS: a new approach to the application of theorem proving to problem solving
IJCAI'71 Proceedings of the 2nd international joint conference on Artificial intelligence
Using and refining simplifications: explanation-based learning of plans in intractable domains
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Selectively generalizing plans for problem-solving
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
Universal plans for reactive robots in unpredictable environments
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 2
| Hi-index | 0.00 |
This paper presents an explanation-based learning strategy for learning general plans for use in an integrated approach to planning. The integrated approach augments a classical planner with the ability to defer achievable goals, thus preserving the construction of provably-correct plans while gaining the ability to utilize runtime information in planning. Proving achievability is shown to be possible without having to determine the actions to achieve the associated goals. A learning strategy called contingent explanation-based learning uses conjectured variables to represent the eventual values of plan parameters with unknown values a priori, and completers to determine these values during execution. An implemented system demonstrates the use of contingent EBL in learning a general completable reactive plan for spaceship acceleration.