Presentation on theme: "Informed Search Reading: Chapter 4.5. 2 Agenda Introduction of heuristic search Greedy search Examples: 8 puzzle, word puzzle A* search Algorithm, admissable."— Presentation transcript:
Informed Search Reading: Chapter 4.5
2 Agenda Introduction of heuristic search Greedy search Examples: 8 puzzle, word puzzle A* search Algorithm, admissable heuristics, optimality Heuristics for other problems Homework
3 8-puzzle URLS Search/Product Search/Product
5 Breadth first Algorithm
10 Depth-first Algorithm
19 Heuristics Suppose 8-puzzle off by one Is there a way to choose the best move next? Good news: Yes! We can use domain knowledge or heuristic to choose the best move
21 Nature of heuristics Domain knowledge: some knowledge about the game, the problem to choose Heuristic: a guess about which is best, not exact Heuristic function, h(n): estimate the distance from current node to goal
22 Heuristic for the 8-puzzle # tiles out of place (h 1 ) Manhattan distance (h 2 ) Sum of the distance of each tile from its goal position Tiles can only move up or down city blocks
Goal State h 1 =1 h 2 =1 h 1 =5 h 2 = =7
24 Best first searches A class of search functions Choose the best node to expand next Use an evaluation function for each node Estimate of desirability Implementation: sort fringe, open in order of desirability Today: greedy search, A* search
25 Greedy search Evaluation function = heuristic function Expand the node that appears to be closest to the goal
26 Greedy Search OPEN = start node; CLOSED = empty While OPEN is not empty do Remove leftmost state from OPEN, call it X If X = goal state, return success Put X on CLOSED SUCCESSORS = Successor function (X) Remove any successors on OPEN or CLOSED Compute f=heuristic function for each node Put remaining successors on either end of OPEN Sort nodes on OPEN by value of heuristic function End while
27 8-puzzle URLS Search/Product Search/Product
30 Analysis of Greedy Search Like depth-first Not Optimal Complete if space is finite
31 A* Search Try to expand node that is on least cost path to goal Evaluation function = f(n) f(n)=g(n)+h(n) h(n) is heuristic function: cost from node to goal g(n) is cost from initial state to node f(n) is the estimated cost of cheapest solution that passes through n If h(n) is an underestimate of true cost to goal A* is complete A* is optimal A* is optimally efficient: no other algorithm using h(n) is guaranteed to expand fewer states
32 A* Search OPEN = start node; CLOSED = empty While OPEN is not empty do Remove leftmost state from OPEN, call it X If X = goal state, return success Put X on CLOSED SUCCESSORS = Successor function (X) Remove any successors on OPEN or CLOSED Compute f(n)= g(n) + h(n) Put remaining successors on either end of OPEN Sort nodes on OPEN by value of heuristic function End while
33 8-puzzle URLS Search/Product Search/Product
37 Admissable heuristics A heuristic that never overestimates the cost to the goal h 1 and h 2 for the 8 puzzle are admissable heuristics Consistency: the estimated cost of reaching the goal from n is no greater than the step cost of getting to n plus estimated cost to goal from n h(n) <=c(n,a,n)+h(n)
38 Which heuristic is better? Better means that fewer nodes will be expanded in searches h 2 dominates h 1 if h 2 >= h 1 for every node n Intuitively, if both are underestimates, then h 2 is more accurate Using a more informed heuristic is guaranteed to expand fewer nodes of the search space Which heuristic is better for the 8-puzzle?
41 Relaxed Problems Admissable heuristics can be derived from the exact solution cost of a relaxed version of the problem If the rules of the 8-puzzle are relaxed so that a tile can move anywhere, then h 1 gives the shortest solution If the rules are relaxed so that a tile can move to any adjacent square, then h 2 gives the shortest solution Key: the optimal solution cost of a relaxed problem is no greater than the optimal solution cost of the real problem.
42 Heuristics for other problems Problems Shortest path from one city to another Touring problem: visit every city exactly once Challenge: Is there an admissable heuristic for sodoku?
43 End of Class Questions
46 Fill-In Station
47 Language Models Knowledge about what letters in words are most frequent Based on a sample of language What is the probability that A is the first letter Having seen A, what do we predict is the next letter? Tune our language model to a situation 3 letter words only Medical vocabulary Newspaper text
48 Formulation Left-right, top-down as path Choose a letter at a time Heuristic: Based on language model Cost of choosing letter A first = 1 – probability of A next Subtract subsequent probabilities Successor function: Choose next best letter, if 3 rd in a row, is it a word? Goal test?