Presentation on theme: "An Introduction to Artificial Intelligence"— Presentation transcript:
1 An Introduction to Artificial Intelligence Lecture 4a: Informed Search and ExplorationRamin HalavatiIn which we see how information about the state space can prevent algorithms from blundering about the dark.
3 UNINFORMED? Uninformed: To search the states graph/tree using Path Cost and Goal Test.
4 INFORMED? Informed: More data about states such as distance to goal. Best First SearchAlmost Best First SearchHeuristich(n): estimated cost of the cheapest path from n to goal. h(goal) = 0.Not necessarily guaranteed, but seems fine.
5 Greedy Best First Search Compute estimated distances to goal.Expand the node which gains the least estimate.
6 Greedy Best First Search Example Heuristic: Straight Line Distance (HSLD)
8 Properties of Greedy Best First Search Complete?No, can get stuck in loop.Time?O(bm), but a good heuristic can give dramatic improvementSpace?O(bm), keeps all nodes in memoryOptimal?No, it depends
9 A* search Idea: avoid expanding paths that are already expensive Evaluation function f(n) = g(n) + h(n)g(n) = cost so far to reach nh(n) = estimated cost from n to goalf(n) = estimated total cost of path through n to goal
12 Admissible Heuristics h(n) is admissible:if for every node n,h(n) ≤ h*(n),h*(n): the true cost from n to goal.Never Overestimates.It’s Optimistic.Example: hSLD(n) (never overestimates the actual road distance)
13 A* is OptimalTheorem: If h(n) is admissible, A* using TREE-SEARCH is optimalTREE-SEARCH: To re-compute the cost of each node, each time you reach it.GRAPH-SEARCH: To store the costs of all nodes, the first time you reach em.
14 Optimality of A* ( proof ) Suppose some suboptimal goal G2 has been generated and is in the fringe. Let n be an unexpanded node in the fringe such that n is on a shortest path to an optimal goal G.f(G2) = g(G2) since h(G2) = 0g(G2) > g(G) since G2 is suboptimalf(G) = g(G) since h(G) = 0f(G2) > f(G) from aboveh(n) ≤ h* (n) since h is admissibleg(n) + h(n) ≤ g(n) + h*(n)f(n) ≤ f(G)Hence f(G2) > f(n), and A* will never select G2 for expansion
15 Consistent Heuristics h(n) is consistent if:for every node n,every successor n' of n generated by any action a,h(n) ≤ c(n,a,n') + h(n')Consistency:MonotonicityTriangular Inequality.Usually at no extra cost!Theorem: If h(n) is consistent, A* using GRAPH-SEARCH is optimal
16 Optimality of A* A* expands nodes in order of increasing f value Gradually adds "f-contours" of nodesContour i has all nodes with f=fi, where fi < fi+1
17 Properties of A*Complete? Yes (unless there are infinitely many nodes with f ≤ f(G) )Time? ExponentialSpace? Keeps all nodes in memory (bd)Optimal? YesA* prunes all nodes with f(n)>f(Goal).A* is Optimally Efficient.
18 How to Design Heuristics? E.g., for the 8-puzzle:h1(n) = number of misplaced tilesh2(n) = total Manhattan distance(i.e., no. of squares from desired location of each tile)
19 Admissible heuristics h1(n) = Number of misplaced tilesh2(n) = Total Manhattan distance
20 Effective Branching Factor If A* finds the answerby expanding N nodes,using heuristic h(n),At depth d,b* is effective branching factor if:1+b*+(b*)2+…+(b*)d = N+1
21 Dominance If h2(n) ≥ h1(n) for all n (both admissible) then h2 dominates h1.h2 is better for search.h2 is more realistic.h (n)=max(h1(n), h2(n),… ,hm(n))Heuristic must be efficient.
22 How to Generate Heuristics? Formal MethodsRelaxed ProblemsPattern Data BasesDisjoint Pattern SetsLearningABSOLVER, 1993A new, better heuristic for 8 puzzle.First heuristic for Rubik’s cube.
23 “Relaxed Problem” Heuristic A problem with fewer restrictions on the actions.The cost of an optimal solution to a relaxed problem is an admissible heuristic for the original problem.8-puzzle:Main Rule:A tile can be moved from square A to B if A is horizontally or vertically adjacent to B and B is empty.Relaxed Rules:A tile can move from square A to square B if A is adjacent to B. (h2)A tile can move from square A to square B if B is blank.A tile can move from square A to square B. (h1)
24 “Sub Problem” Heuristic The cost to solve a subproblem.It IS admissible.
25 “Pattern Database” Heuristics To store the exact solution cost to some sub-problems.
26 “Disjoint Pattern” Databases To add the result of several Pattern-Database heuristics.Speed Up: 103 times for 15-Puzzle and 106 times for 24-Puzzle.Separablity: Rubik’s cube vs. 8-Puzzle.
27 Learning Heuristics from Experience Machine Learning Techniques.Feature SelectionLinear Combinations
28 BACK TO MAIN SEARCH METHOD What’s wrong with A*? It’s both Optimal and Optimally Efficient.MEMORY
29 Memory Bounded Heuristic Search Iterative Deepening A* (IDA*)Similar to Iterative Deepening Depth First SearchBounded by f-cost.Memory: b*d
30 Recursive Best First Search Main Idea:To search a level with limited f-cost, based on other open nodes with continuous update.