Sentence examples for iterative deepening from inspiring English sources

In this paper we prove the stronger result that for agent searching in uniform b-ary trees, iterative deepening is optimal up to lower-order terms.

Moreover, it is enhanced by methods such as recursive iterative deepening, dynamic evaluation, efficient successor ordering, and pruning by dependency relations.

Previous approaches to this problem, such as iterative deepening, do not expand nodes in best-first order if the cost function can decrease along a path.

We present a linear-space best-first search algorithm (RBFS) that always explores new nodes in best-first order, regardless of the cost function, and expands fewer nodes than iterative deepening with a nondecreasing cost function.

In this paper we show that depth-first iterative deepening (DFID) strategies are optimal for an agent searching in a line, in m concurrent rays, and in uniform b-ary trees.

This problem can be overcome by means of the depth-first iterative deepening (DFID) search strategy.

We analyze the time complexity of iterative-deepening-A∗ (IDA∗).

It transforms a best-first PN-search algorithm into an iterative-deepening depth-first approach.

This paper presents an efficient SIMD parallel algorithm, called IDPS (for iterative-deepening parallel search).

We present a parallel implementation of Iterative-Deepening-A∗, a depth-first heuristic search, on the single-instruction, multiple-data (SIMD) Connection Machine★.

The breadth-first heuristic search algorithms introduced in this paper include a memory-efficient implementation of breadth-first branch-and-bound search and a breadth-first iterative-deepening A* algorithm that is based on it.