Learning Recurring Subplans

Ruby, David; Kibler, Dennis

doi:10.1016/b978-1-4832-0774-2.50019-6

Cited by 2 publications

(1 citation statement)

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“…The simple-to-hard order of samples in the training set is based on the observation that simple planning problems are often subproblems of harder problems and therefore learning how to solve simpler problems will potentially be useful in solving more difficult ones. Ruby and Kibler (1993) introduce another system that utilizes the above observation to learn recurring subplans. ILP techniques are also used to learn control rules for BlackBox, a planner that formulates planning problems as constraint satisfaction problems (Huang et al, 2000).…”

Section: Related Workmentioning

confidence: 99%

Learning approximate preconditions for methods in hierarchical plans

Ilghami

Muñoz-Ávila

Nau

et al. 2005

Proceedings of the 22nd International Conference on Machine Learning - ICML '05

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A significant challenge in developing planning systems for practical applications is the difficulty of acquiring the domain knowledge needed by such systems. One method for acquiring this knowledge is to learn it from plan traces, but this method typically requires a huge number of plan traces to converge. In this paper, we show that the problem with slow convergence can be circumvented by having the learner generate solution plans even before the planning domain is completely learned. Our empirical results show that these improvements reduce the size of the training set that is needed to find correct answers to a large percentage of planning problems in the test set.

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Section: Related Workmentioning

confidence: 99%