Depth-first iterative-deepening: an optimal admissible tree search
Artificial Intelligence
Artificial intelligence
Parallel bidirectional search using multidimensional heuristics
Parallel bidirectional search using multidimensional heuristics
An Improved Bidirectional Heuristic Search Algorithm
Journal of the ACM (JACM)
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A heuristic improvement technique referred to as multi-dimensional heuristics is presented. Instead of only applying the heuristic between two states X1/X1X2 and X2, when a distance estimate of is needed, this technique uses a reference state R and applies the heuristic function to (X1,R) and (X'2,R) and compares the resulting values. If two states are close to each other, then they should also be approximately equidistant to a third reference state. It is possible to use many such reference states to improve some heuristics. The reference states are used to map the search into an N-dimensional search space. The process of choosing reference states can be automated and is in fact a learning procedure. Test results using the 15-puzzle are presented in support of the effectiveness of multi-dimensional heuristics. This method has been shown to improve both a weak 15-puzzle heuristic, the tile reversal heuristic, as well as the stronger Manhattan distance heuristic.