Multi-Heuristic A*
International Journal of Robotics Research(2016)
摘要
The performance of heuristic search-based planners depends heavily on the quality of the
heuristic function used to focus the search. These algorithms work fast and generate
high-quality solutions, even for high-dimensional problems, as long as they are given a
well-designed heuristic function. On the other hand, their performance can degrade
considerably if there are large heuristic depression regions, i.e. regions in the search
space where heuristic values do not correlate well with the actual cost-to-goal values.
Consequently, the research in developing an efficient planner for a specific domain
becomes the design of a good heuristic function. However, for many domains, it is hard to
design a single heuristic function that captures all of the complexities
of the problem. Furthermore, it is hard to ensure that heuristics are admissible (provide
lower bounds on the cost-to-goal) and consistent, which is necessary for A* like searches
to provide guarantees on completeness and bounds on sub-optimality. In this paper, we
develop a novel heuristic search, called Multi-Heuristic A* (MHA*), that takes in multiple, arbitrarily inadmissible heuristic functions in addition to a
single consistent heuristic, and uses all of them simultaneously to search in a way that
preserves guarantees on completeness and bounds on
sub-optimality . This enables the search to combine very effectively the guiding
powers of different heuristic functions and simplifies dramatically the process of
designing heuristic functions by a user because these functions no longer need to be
admissible or consistent. We support these claims with experimental analysis on several
domains ranging from inherently continuous domains such as full-body manipulation and
navigation to inherently discrete domains such as the sliding tile puzzle.
更多查看译文
关键词
Path planning,heuristic search,bounded sub-optimal planning,full-body manipulation,sliding tile puzzle
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要