A* in Clojure

unsorted — cgrand, 4 September 2010 @ 4 h 22 min

Thanks to Mark Engelberg there’s now a nice priority map in contrib. With such a facility, it becomes really easy to write a nice implementation of the A* algorithm.

(ns net.cgrand.clj-me.a-star
  (:use [clojure.contrib.priority-map :only [priority-map]]))

(defn A*
 "Finds a path between start and goal inside the graph described by edges
  (a map of edge to distance); estimate is an heuristic for the actual
  distance. Accepts a named option: :monotonic (default to true).
  Returns the path if found or nil."
 [edges estimate start goal & {mono :monotonic :or {mono true}}]
  (let [f (memoize #(estimate % goal)) ; unsure the memoization is worthy
        neighbours (reduce (fn [m [a b]] (assoc m a (conj (m a #{}) b)))
                      {} (keys edges))]
    (loop [q (priority-map start (f start))
           preds {}
           shortest {start 0}
           done #{}]
      (when-let [[x hx] (peek q)]
        (if (= goal x)
          (reverse (take-while identity (iterate preds goal)))
          (let [dx (- hx (f x))
                bn (for [n (remove done (neighbours x))
                         :let [hn (+ dx (edges [x n]) (f n))
                               sn (shortest n Double/POSITIVE_INFINITY)]
                         :when (< hn sn)]
                     [n hn])]
            (recur (into (pop q) bn)
              (into preds (for [[n] bn] [n x]))
              (into shortest bn)
              (if mono (conj done x) done))))))))

Example:

(defn euclidian-distance [a b] ; multidimensional
  (Math/sqrt (reduce + (map #(let [c (- %1 %2)] (* c c)) a b))))

;; generate a grid graph whose outlying edges are one-way
(defn grid [x y w h]
  (into {}
    (for [i (range w) j (range h)
          :let [x0 (+ x i) y0 (+ y j) x1 (inc x0) y1 (inc y0)]]
      {[[x0 y0] [x1 y0]] 1
       [[x1 y0] [x1 y1]] 1
       [[x1 y1] [x0 y1]] 1
       [[x0 y1] [x0 y0]] 1})))

(def g (apply dissoc (grid 0 0 4 4) (keys (grid 1 1 2 2))))

(comment 
user=> (A* g euclidian-distance [0 3] [4 2])
([0 3] [1 3] [2 3] [3 3] [3 2] [4 2])
)

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11 Comments »

  1. Hey, I have generic AI search (including A*) in clojure (http://github.com/aria42/mochi/blob/master/src/mochi/search.clj)
    and it was using java.util.PriorityQueue, clojure.contrib.priority-map would be much better.

    Comment by Aria Haghighi — 4 September 2010 @ 5 h 42 min
  2. This appears to be not A* but a simplified algorithm which assumes that your heuristic is monotonic.

    Comment by foo — 5 September 2010 @ 23 h 17 min
  3. @foo I may have commited an oversight in implementing A* so I’d really like to hear more from you. However note that the closed set is augmented (monotonic A*) only when the option :monotonic is set to true.

    Comment by cgrand — 6 September 2010 @ 10 h 04 min
  4. it’s still moonspeak.

    Comment by ghlugh — 7 September 2010 @ 7 h 36 min
  5. [...] may also be interesting to compare this a* search function to that of cgrande’s. a* algorithm, dijkstra's algorithm, [...]

  6. Thanks for the nice example. I couple of minor comments:
    The Math/sqrt is kinda slow and seems unnecessary in Eucl-dist; and if all the costs were integers, one could avoid Math/sqrt and replace the Double constant in A* with a Long (cheaper comparisons?).
    The memoize is indeed worthy (question you posed above) as lookups of the same node may be done multiple times (node x may be rejected once but selected at a later time). I wonder though whether memoize is a better choice than sticking values into a regular hash-map.(?)

    Comment by Vish — 30 January 2012 @ 22 h 57 min
  7. Hi,
    Thanks for your post, insightful as usual ☺.

    Just passing by, I wondered if you had considered using cheplist [*].

    Best Regards,

    B.

    [*]http://nakkaya.com/2011/10/10/cheaplist-in-clojure/

    Comment by bernard — 7 March 2013 @ 18 h 09 min
  8. @Bernard, no I haven’t, merci pour le pointeur.

    Comment by cgrand — 8 March 2013 @ 14 h 15 min
  9. This is brilliant and beautiful!

    My favorite part is the code to trace back through the preds to recover the path: (reverse (take-while identity (iterate preds goal))) … what could be more succinct?

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