Euclidean Heuristic Optimization

  title={Euclidean Heuristic Optimization},
  author={D. Chris Rayner and Michael H. Bowling and Nathan R. Sturtevant},
We pose the problem of constructing good search heuristics as an optimization problem: minimizing the loss between the true distances and the heuristic estimates subject to admissibility and consistency constraints. For a well-motivated choice of loss function, we show performing this optimization is tractable. In fact, it corresponds to a recently proposed method for dimensionality reduction. We prove this optimization is guaranteed to produce admissible and consistent heuristics, generalizes… CONTINUE READING
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