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Learning to Solve Complex Planning Problems: Finding Useful Auxiliary Problems.
Peter
Stone and Manuela Veloso.
In Technical Report of the AAAI 1994 Fall Symposium
on Planning and Learning: On to Real Applications, pp. 137–141, New Orleans, LA, November 1994.
[PDF]52.6kB [postscript]56.8kB
Learning from past experience allows a problem solver to increase its solvability horizon from simple to complex problems. For planners, learning involves a training phase during which knowledge is extracted from simple problems. But how are these simple problems constructed? All current learning and problem solving systems require the user to provide the training set. However it is rarely easy to identify problems that are both simple and useful for learning, especially in complex applications. In this paper, we present our initial research towards the automated or semi-automated identification of these simple problems. From a difficult problem and a corresponding partially completed search episode, we extract auxiliary problems with which to train the learner. We motivate this overlooked issue, describe our approach, and illustrate it with examples.
@InProceedings(aaai-fall94, Author="Peter Stone and Manuela Veloso",
Title="Learning to Solve Complex Planning Problems: Finding Useful Auxiliary Problems",
BookTitle="Technical Report of the AAAI 1994 Fall Symposium on
Planning and Learning: On to Real Applications",
Year="1994", Month="November", Pages="137--141",
Address="New Orleans, LA",
abstract={
Learning from past experience allows a problem
solver to increase its solvability horizon from
simple to complex problems. For planners, learning
involves a training phase during which knowledge is
extracted from simple problems. But how are these
simple problems constructed? All current learning
and problem solving systems require the user to
provide the training set. However it is rarely easy
to identify problems that are both simple and useful
for learning, especially in complex applications.
In this paper, we present our initial research
towards the automated or semi-automated
identification of these simple problems. From a
difficult problem and a corresponding partially
completed search episode, we extract auxiliary
problems with which to train the learner. We
motivate this overlooked issue, describe our
approach, and illustrate it with examples.
},
)
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