Anytime Heuristic Search

Anytime Heuristic Search

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...

Anytime Heuristic Search: First Results

Anytime heuristic search for partial satisfaction planning

We present a heuristic search approach to solve partial satisfaction planning (PSP) problems. In these problems, goals are modeled as soft constraints with utility values, and actions have costs. Goal utility represents the value of each goal to the user and action cost represents the total resource cost (e.g., time, fuel cost) needed to execute each action. The objective is to find the plan th...

Anytime Heuristic Search: Frameworks and Algorithms

Anytime search is a pragmatic approach for trading solution cost and solving time. It can also be used for solving problems within a time bound. Three frameworks for constructing anytime algorithms from bounded suboptimal search have been proposed: continuing search, repairing search, and restarting search, but what combination of suboptimal search and anytime framework performs best? An extens...

Potential Search: A New Greedy Anytime Heuristic Search

In this paper we explore a novel approach for anytime heuristic search, in which the node that is most probable to improve the incumbent solution is expanded first. This is especially suited for the ”anytime aspect” of anytime algorithms the possibility that the algorithm will be be halted anytime throughout the search. The potential of a node to improve the incumbent solution is estimated by a...