SMA*

SMA* or Simplified Memory Bounded A* is a shortest path algorithm based on the A* algorithm. The main advantage of SMA* is that it uses a bounded memory, while the A* algorithm might need exponential memory. All other characteristics of SMA* are inherited from A*.

Properties

SMA* has the following properties

  • It works with a heuristic, just as A*
  • It is complete if the allowed memory is high enough to store the shallowest solution
  • It is optimal if the allowed memory is high enough to store the shallowest optimal solution, otherwise it will return the best solution that fits in the allowed memory
  • It avoids repeated states as long as the memory bound allows it
  • It will use all memory available
  • Enlarging the memory bound of the algorithm will only speed up the calculation
  • When enough memory is available to contain the entire search tree, then calculation has an optimal speed

Implementation

The implementation of Simple memory bounded A* is very similar to that of A*; the only difference is that nodes with the highest f-cost are pruned from the queue when there isn't any space left. Because those nodes are deleted, simple memory bounded A* has to remember the f-cost of the best forgotten child of the parent node. When it seems that all explored paths are worse than such a forgotten path, the path is regenerated.[1]

Pseudo code:

function simple memory bounded A*-star(problem): path
  queue: set of nodes, ordered by f-cost;
begin
  queue.insert(problem.root-node);

  while True do begin
    if queue.empty() then return failure; //there is no solution that fits in the given memory
    node := queue.begin(); // min-f-cost-node
    if problem.is-goal(node) then return success;
    
    s := next-successor(node)
    if !problem.is-goal(s) && depth(s) == max_depth then
        f(s) := inf; 
        // there is no memory left to go past s, so the entire path is useless
    else
        f(s) := max(f(node), g(s) + h(s));
        // f-value of the successor is the maximum of
        //      f-value of the parent and 
        //      heuristic of the successor + path length to the successor
    end if
    if no more successors then
       update f-cost of node and those of its ancestors if needed
    
    if node.successors ⊆ queue then queue.remove(node); 
    // all children have already been added to the queue via a shorter way
    if memory is full then begin
      bad Node := shallowest node with highest f-cost;
      for parent in bad Node.parents do begin
        parent.successors.remove(bad Node);
        if needed then queue.insert(parent); 
      end for
    end if

    queue.insert(s);
  end while
end

References
  1. Russell, S. (1992). "Efficient memory-bounded search methods". In Neumann, B. (ed.). Proceedings of the 10th European Conference on Artificial intelligence. Vienna, Austria: John Wiley & Sons, New York, NY. pp. 1–5. CiteSeerX 10.1.1.105.7839.
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