Iterative Budgeted Exponential Search
This addresses efficiency issues in heuristic search for AI and planning, offering a potential general replacement for IDA* with incremental improvements.
The paper tackles the problem of excessive re-expansions in heuristic search algorithms like A* and IDA*, proposing a new algorithmic framework that reduces expansions to O(n log C) compared to existing bounds of Ω(2^n) or Ω(n^2), with experiments showing robustness in failure scenarios and no overhead over IDA* in suitable cases.
We tackle two long-standing problems related to re-expansions in heuristic search algorithms. For graph search, A* can require $Ω(2^{n})$ expansions, where $n$ is the number of states within the final $f$ bound. Existing algorithms that address this problem like B and B' improve this bound to $Ω(n^2)$. For tree search, IDA* can also require $Ω(n^2)$ expansions. We describe a new algorithmic framework that iteratively controls an expansion budget and solution cost limit, giving rise to new graph and tree search algorithms for which the number of expansions is $O(n \log C)$, where $C$ is the optimal solution cost. Our experiments show that the new algorithms are robust in scenarios where existing algorithms fail. In the case of tree search, our new algorithms have no overhead over IDA* in scenarios to which IDA* is well suited and can therefore be recommended as a general replacement for IDA*.