Exponential-Binary State-Space Search
This incremental improvement addresses efficiency issues in state-space search algorithms like IDA* and breadth-first heuristic search for applications with unknown cost bounds.
The paper tackles the problem of iterative deepening search overshooting or undershooting cost bounds by proposing exponential-binary state-space search, which interleaves exponential and binary searches to reduce worst-case overhead from polynomial to logarithmic.
Iterative deepening search is used in applications where the best cost bound for state-space search is unknown. The iterative deepening process is used to avoid overshooting the appropriate cost bound and doing too much work as a result. However, iterative deepening search also does too much work if the cost bound grows too slowly. This paper proposes a new framework for iterative deepening search called exponential-binary state-space search. The approach interleaves exponential and binary searches to find the desired cost bound, reducing the worst-case overhead from polynomial to logarithmic. Exponential-binary search can be used with bounded depth-first search to improve the worst-case performance of IDA* and with breadth-first heuristic search to improve the worst-case performance of search with inconsistent heuristics.