A*

Implementation of the A* Search algorithm.

Install

$ go get github.com/pietv/astar

Overview

A* Search is one of the intelligent exhaustive search algorithms which gets from Start to Finish by exploring successor states.

It's intelligent because it uses a special guidance in selecting the states that are going to be explored next. The algorithm uses a minimum value of a sum of the next successor cost and the heuristic estimate (distance, for example) of how close that next successor is to Finish. These values are named Cost and Estimate in this implementation.

Without any guidance (that is when both Cost and Estimate values are constant), it explores all successor states, making it essentially the Breadth First Search algorithm (Go's container/heap implementation behaves like a queue if keys are equal).

Depending on whether Cost and Estimate are constant or not, A* Search behaves like other well-known algorithms:

Cost	Estimate	Behavior
const	const	Breadth First Search
variable	const	[Dijkstra's Shortest Path] (http://en.wikipedia.org/wiki/Dijkstra%27s_algorithm) / Uniform-Cost Search
const	variable	Greedy Best First Search
variable	variable	A*

It is not necessary to use a graph data structure. Crawling the internet and feeding harvested links as successors would do or, as another example, the provided maze demo uses a rectangular matrix and uses surrounding cells as successors.

Install the demo:

$ go get github.com/pietv/astar/cmd/maze