Time-bounded best-first search for reversible and non-reversible search graphs

Abstract

Time-Bounded A∗ is a real-time, single-agent, deterministic search algorithm that expands states of a graph in the same order as A∗ does, but that unlike A∗ interleaves search and action execution. Known to outperform state-of-the-art real-time search algorithms based on Korf's Learning Real-Time A∗ (LRTA∗) in some benchmarks, it has not been studied in detail and is sometimes not considered as a "true" real-time search algorithm since it fails in non-reversible problems even it the goal is still reachable from the current state. In this paper we propose and study Time-Bounded Best-First Search (TB(BFS)) a straightforward generalization of the time-bounded approach to any best-first search algorithm. Furthermore, we propose Restarting Time-Bounded Weighted A∗ (TBR (WA∗)), an algorithm that deals more adequately with non-reversible search graphs, eliminating "backtracking moves" and incorporating search restarts and heuristic learning. In non-reversible problems we prove that TB(BFS) terminates and we deduce cost bounds for the solutions returned by Time-Bounded Weighted A∗ (TB(WA∗)), an instance of TB(BFS). Furthermore, we prove TBR (WA∗), under reasonable conditions, terminates. We evaluate TB(WA) in both grid pathfinding and the 15-puzzle. In addition, we evaluate TBR (WA∗) on the racetrack problem. We compare our algorithms to LSS-LRTWA∗, a variant of LRTA∗ that can exploit lookahead search and a weighted heuristic. A general observation is that the performance of both TB(WA∗) and TBR (WA∗) improves as the weight parameter is increased. In addition, our time-bounded algorithms almost always outperform LSS-LRTWA∗ by a significant margin.