Using heuristics for greedy search and A* search

Search algorithms with heuristics

Find absolute distance from goal for each node. choose node with shortest distance.

Cost of each is \(f(n)=h(n)\), where \(h(n)\) is the heuristic cost of node \(n\).

If the heuristic is admissible, then a* is optimal. Intuitively because the the heuristic steers away from any suboptimal solutions.

Admissible? For all nodes n , h(n)<=h*(n). where h* is true cost

\(f(n)=g(n)+h(n)\)

\(g(n)\) is the cost to reach \(n\) from the current position.

Informed: Yes

Time: Exponential

Space: Big, all nodes kept in memory

Complete: Yes

Optimal: Yes, if the heuristic is admissible

Iterative deepening A*

Identifying heuristics

Generating heuristics for search. we can losen restriction to get a much simpler problem and rank moves by how good they are for loosened problem.

eg for for path to goal, assume can go directly from next place in a straight line.