Game-map Pathfinding with Per-Problem Selection of Synthesized Heuristics

Variants of A* search are widely used for video-game pathfinding with a heuristic function that is typically either a generic formula designed by humans (e.g., the Manhattan distance) or pre-computed for a specific video-game map. Recent work attempted to combine portability of the former and higher performance of the latter by automatically synthesizing arithmetic formulae. Such formulae are simple enough to be human-readable, portable enough to provide guidance on novel maps and yet complex enough to notably outperform a baseline. Each formula-represented heuristic was synthesized for a given map, presumably capturing some features of the map. However, maps can be non-uniform and some regions of one map may have features similar to another map. This work uses a portfolio of synthesized heuristics and selects from it on a per-problem basis. The selection is done automatically by determining the pair of map regions in which the start and the goal states of a given problem instance belong. A pre-computed database gives the highest-performing heuristic from the portfolio for that pair of regions. This heuristic is then used to guide A* to solve the problem instance. Empirical evaluation on maps from video games indicates noticeable speed-up compared to using a single synthesized heuristic for all problem instances on a map.