Learning general completable reactive plans
Melinda T. Gervasio · National Conference on Artificial Intelligence · 1990
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.