Anytime Heuristic Search

TL;DR

This paper introduces an anytime heuristic search algorithm based on A* that quickly finds approximate solutions and improves them over time, balancing solution quality and computational effort, with applications in various domains.

Contribution

It presents a novel method to convert A* into an anytime algorithm using weighted heuristic search, and extends the approach to Recursive Best-First Search (RBFS).

Findings

Effective in sliding-tile puzzles, STRIPS planning, and sequence alignment.

Provides a flexible tradeoff between search time and solution quality.

Demonstrates generality by transforming RBFS into an anytime algorithm.

Abstract

We describe how to convert the heuristic search algorithm A* into an anytime algorithm that finds a sequence of improved solutions and eventually converges to an optimal solution. The approach we adopt uses weighted heuristic search to find an approximate solution quickly, and then continues the weighted search to find improved solutions as well as to improve a bound on the suboptimality of the current solution. When the time available to solve a search problem is limited or uncertain, this creates an anytime heuristic search algorithm that allows a flexible tradeoff between search time and solution quality. We analyze the properties of the resulting Anytime A* algorithm, and consider its performance in three domains; sliding-tile puzzles, STRIPS planning, and multiple sequence alignment. To illustrate the generality of this approach, we also describe how to transform the memory-efficient search algorithm Recursive Best-First Search (RBFS) into an anytime algorithm.