Learning from Solution Paths: An Approach to the Credit Assignment Problem
AbstractIn this article we discuss a method for learning useful conditions on the application of operators during heuristic search. Since learning is not attempted until a complete solution path has been found for a problem, credit for correct moves and blame for incorrect moves is easily assigned. We review four learning systems that have incorporated similar techniques to learn in the domains of algebra, symbolic integration, and puzzle-solving. We conclude that the basic approach of learning from solution paths can be applied to any situation in which problems can be solved by sequential search. Finally, we examine some potential difficulties that may arise in more complex domains, and suggest some possible extensions for dealing with them.
How to Cite
Sleeman, D., Langley, P., & Mitchell, T. M. (1982). Learning from Solution Paths: An Approach to the Credit Assignment Problem. AI Magazine, 3(2), 48. https://doi.org/10.1609/aimag.v3i2.372
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