"' Incremental heuristic search "'algorithms combine both incremental and heuristic search to speed up searches of sequences of similar search problems, which is important in domains that are only incompletely known or change dynamically.
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Some techniques for determining the upper and lower bounds for the snake-in-the-box problem include proofs using discrete mathematics and graph theory, exhaustive search of the search space, and heuristic search utilizing evolutionary techniques.
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Although the Symposium on Combinatorial Search is not among the most important Artificial Intelligence conferences in the world ( yet ! ), it is the most prominent reference to all researchers in the field of combinatorial optimization and heuristic search.
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The ideas behind his incremental heuristic search algorithm D * Lite, for example, have been incorporated by others into a variety of path planning systems in robotics, including Carnegie Mellon University's winning entry in the DARPA Urban Challenge.
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After his dissertation, Koenig laid a broad foundation for incremental heuristic search in artificial intelligence with the development of search algorithms such as Lifelong Planning A * ( LPA * ), D * Lite, Adaptive A * ( AA * ) and Fringe-Saving A * ( FSA * ).
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However, this is only possible for a relatively small number of sequences or species because the problem of identifying the most parsimonious tree is known to be NP-hard; consequently a number of heuristic search methods for optimization have been developed to locate a highly parsimonious tree, if not the best in the set.
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