.png){kind=icon}
.png){kind=icon}
Return
Probably bounded suboptimal heuristic search
.png){kind=icon}
OA
AI
DOI：10.1016/j.artint.2018.08.005.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Finding an optimal solution to a search problem is often desirable, but can be too difficult in many cases. A common approach in such cases is to try to find a solution whose suboptimality is bounded, where a parameter epsilon defines how far from optimal a solution can be while still being acceptable. A scarcely studied alternative is to try to find a solution that is probably optimal, where a parameter delta defines the confidence required in the solution's optimality. This paper explores this option and introduces the concept of a probably bounded-suboptimal search (pBS search) algorithm. Such a search algorithm accepts two parameters, epsilon and delta, and outputs a solution that with probability at least 1 - delta costs at most 1 + epsilon times the optimal solution. A general algorithmic framework for pBS search algorithms is proposed. Several instances of this framework are described and analyzed theoretically and experimentally on a range of search domains. Results show that pBS search algorithms are often faster than a state-of-the-art bounded-suboptimal search algorithm. This shows in practice that finding solutions that satisfy a given suboptimality bound with high probability can be done faster than finding solutions that satisfy the same suboptimality bound with certainty. (C) 2018 Elsevier B.V. All rights reserved.
Keywords:
Artificial intelligence
Heuristic search
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
13.9
Papers:
6.1K
Citations:
1.9W