Escaping Heuristic Hollows in Real-Time Search without Learning

Abstract

Real-time search is a standard approach to solving search problems in which agents have limited sensing capabilities and must act quickly. It is well known that real-time search algorithms like LRTA* and RTA* perform poorly in regions of the search space in which the heuristic function is very imprecise. Approaches that use look ahead or learning are used to overcome this drawback. They perform more computation in the planning phase compared to LRTA* andRTA* . In this paper we propose Path Real-Time A* (PRTA* ), an algorithm that, like LRTA*, performs little computation in the planning phase, but that, unlike LRTA*, terminates even if the problem does not have a solution. We show that our algorithm outperforms LRTA* and RTA* in standard real-time benchmark problems. Furthermore, we show that in some cases, PRTA* may also outperform lookahead-or learning-enabled algorithms but carrying out significantly less computation.