Peter Stone's Selected Publications

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Learning to Solve Complex Planning Problems: Finding Useful Auxiliary Problems

Peter Stone and Manuela Veloso. Learning to Solve Complex Planning Problems: Finding Useful Auxiliary Problems. 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.

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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.

BibTeX Entry

@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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