A-MHA*: Anytime Multi-Heuristic A*

Abstract

Designing good heuristic functions for graph search requires adequate domain knowledge. It is often easy to design heuristics that perform well and correlate with the underlying true cost-to-go values in certain parts of the search space but these may not be admissible throughout the domain thereby affecting the optimality guarantees of the search. Bounded suboptimal search using several of such partially good but inadmissible heuristics was developed in Multi-Heuristic A* (MHA*). Although MHA* leverages multiple inadmissible heuristics to potentially generate a faster suboptimal solution, the original version does not improve the solution over time. It is an one shot algorithm that requires careful setting of inflation factors to obtain a desired one time solution. In this work, we tackle this issue by extending MHA* to an anytime version that finds a feasible suboptimal solution quickly and continually improve it until time runs out. Our work is inspired from the Anytime Repairing A* (ARA*) algorithm. We prove that our precise adaptation of ARA* concepts in the MHA* framework preserves the original suboptimal and completeness guarantees and enhances MHA* to perform in an anytime fashion. Furthermore, we report the performance of A-MHA* in 3-D path planning domain and sliding tiles puzzle and compare against MHA* and other anytime algorithms.

Learning configurations of wires for real-time shape estimation and manipulation planning

“…To achieve anytime characteristics in heuristic search, typical approaches involve obtaining fast initial solution through algorithms like beam search, and then progressively increase the number of expandable nodes at a given depth level [14]- [17]. Alternatively, and widely used in robotics, anytime algorithms often incrementally adjust the heuristic inflation [18]- [23]. Nonetheless, none of the above provide planning time bounds for the initial solution.…”

A-ePA*SE: Anytime Edge-Based Parallel A* for Slow Evaluations

“…A more elegant anytime algorithm Anytime Repairing A* (ARA*) (Likhachev, Gordon, and Thrun 2003) reuses previous search efforts to prevent redundant work by keeping track of states whose cost-to-come can be further reduced in future iterations. There are several other anytime algorithms (Natarajan et al 2019;Aine et al 2016), none of which utilize any parallelization.…”