Heuristics used for solving hard real-time search problems have regions with depressions. Such regions are bounded areas of the search space in which the heuristic function is inaccurate compared to the actual cost to reach a solution. Early real-time search algorithms, like LRTA, easily become trapped in those regions since the heuristic values of their states may need to be updated multiple times, which results in costly solutions. State-of-the-art real-time search algorithms, like LSS-LRTA or LRTA(k), improve LRTA's mechanism to update the heuristic, resulting in improved performance. Those algorithms, however, do not guide search towards avoiding depressed regions. This paper presents depression avoidance, a simple real-time search principle to guide search towards avoiding states that have been marked as part of a heuristic depression. We propose two ways in which depression avoidance can be implemented: mark-and-avoid and move-to-border. We implement these strategies on top of LSS-LRTA and RTAA, producing 4 new real-time heuristic search algorithms: aLSS-LRTA, daLSS-LRTA, aRTAA, and daRTAA. When the objective is to nd a single solution by running the real-time search algorithm once, we show that daLSS-LRTA and daRTAA outperform their predecessors sometimes by one order of magnitude. Of the four new algorithms, daRTAA produces the best solutions given a xed deadline on the average time allowed per planning episode. We prove all our algorithms have good theoretical properties: in nite search spaces, they nd a solution if one exists, and converge to an optimal after a number of trials.
Áreas temáticas de ASJC Scopus
- Inteligencia artificial