3 Review: Tree searchA search strategy is defined by picking the order of node expansion
4 Best-first search Idea: use an evaluation function f(n) for each node estimate of "desirability“Expand most desirable unexpanded nodeImplementation:Order the nodes in fringe in decreasing order of desirabilitySpecial cases:greedy best-first searchA* search
6 Greedy best-first search Evaluation function f(n) = h(n) (heuristic) = estimate of cost from n to goal e.g., hSLD(n) = straight-line distance from n to BucharestGreedy best-first search expands the node that appears to be closest to goal
11 Properties of greedy best-first search Complete? No – can get stuck in loops, e.g., Iasi Neamt Iasi Neamt Time? O(bm), but a good heuristic can give dramatic improvementSpace? O(bm) -- keeps all nodes in memoryOptimal? No
12 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
19 Admissible heuristics A heuristic h(n) is admissible if for every node n,h(n) ≤ h*(n), where h*(n) is the true cost to reach the goal state from n.An admissible heuristic never overestimates the cost to reach the goal, i.e., it is optimisticExample: hSLD(n) (never overestimates the actual road distance)Theorem: If h(n) is admissible, A* using TREE-SEARCH is optimal
20 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 above
21 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) > 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
22 Consistent heuristics A heuristic 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')If h is consistent, we havef(n') = g(n') + h(n')= g(n) + c(n,a,n') + h(n')≥ g(n) + h(n)= f(n)i.e., f(n) is non-decreasing along any path.Theorem: If h(n) is consistent, A* using GRAPH-SEARCH is optimal
23 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
24 Properties of A*Complete? Yes (unless there are infinitely many nodes with f ≤ f(G) )Time? ExponentialSpace? Keeps all nodes in memoryOptimal? Yes
26 Heuristics and A* Search The heuristic function h(n) tells A* an estimate of the minimum cost from any vertex n to the goal.The heuristic can be used to control A*’s behavior.If h(n) is 0, then only g(n) plays a role, and A* turns into Dijkstra’s algorithmIf h(n) is always lower than (or equal to) the cost of moving from n to the goal, then A* is guaranteed to find a shortest path.If h(n) is exactly equal to the cost of moving from n to the goal, then A* will only follow the best path and never expand anything else, making it very fast.
27 Heuristics and A* Search The heuristic can be used to control A*’s behavior.If h(n) is sometimes greater than the cost of moving from n to the goal, then A* is not guaranteed to find a shortest path, but it can run faster.At the other extreme, if h(n) is very high relative to g(n), then only h(n) plays a role, and A* turns into Greedy Best-First-Search.
28 Heuristics and A* Search Suppose your game has two types of terrain, Flat and MountainMovement costs are 1 for flat land and 3 for mountainsA* is going to search three times as far along flat land as it does along mountainous landYou can speed up A*’s search by using 1.5 as the heuristic distance between two map spaces.Alternatively, you can speed up up A*’s search by decreasing the amount it searches for paths around mountains―just tell A* that the movement cost on mountains is 2 instead of 3.Now it will search only twice as far along the flat terrain as along mountainous terrain. Either approach gives up ideal paths to get something quicker.
29 HeuristicsHeuristic (/hjʉˈrɪstɨk/; Greek: "Εὑρίσκω", "find" or "discover") refers to experience-based techniques for problem solving, learning, and discovery that find a solution, which is not guaranteed to be optimal, but good enough for a given set of goals. (Wikipedia)Trial and Error
30 Polya – How to solve itIf you are having difficulty understanding a problem, try drawing a picture.If you can't find a solution, try assuming that you have a solution and seeing what you can derive from that ("working backward").If the problem is abstract, try examining a concrete example.Try solving a more general problem first (the "inventor's paradox": the more ambitious plan may have more chances of success).
31 Admissible 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)h1(S) = ?h2(S) = ?
33 Admissible 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)h1(S) = ? 8h2(S) = ? = 18
34 DominanceIf h2(n) ≥ h1(n) for all n (both admissible) then h2 dominates h1h2 is better for searchTypical search costs (average number of nodes expanded):d=12 IDS = 3,644,035 nodes A*(h1) = 227 nodes A*(h2) = 73 nodesd=24 IDS = too many nodes A*(h1) = 39,135 nodes A*(h2) = 1,641 nodes
35 Relaxed problemsA problem with fewer restrictions on the actions is called a relaxed problemThe cost of an optimal solution to a relaxed problem is an admissible heuristic for the original problemIf the rules of the 8-puzzle are relaxed so that a tile can move anywhere, then h1(n) gives the shortest solutionIf the rules are relaxed so that a tile can move to any adjacent square, then h2(n) gives the shortest solution
36 Local search algorithms In many optimization problems, the path to the goal is irrelevant; the goal state itself is the solutionState space = set of "complete" configurationsFind configuration satisfying constraints, e.g., n-queensIn such cases, we can use local search algorithms keep a single "current" state, try to improve it
37 Example: n-queensPut n queens on an n × n board with no two queens on the same row, column, or diagonal
38 Hill-climbing search"Like climbing Everest in thick fog with amnesia"
39 Hill-climbing searchProblem: depending on initial state, can get stuck in local maxima
40 Hill-climbing search: 8-queens problem h = number of pairs of queens that are attacking each other, either directly or indirectlyh = 17 for the above state
41 Hill-climbing search: 8-queens problem A local minimum with h = 1
42 Simulated annealing search Idea: escape local maxima by allowing some "bad" moves but gradually decrease their frequency
43 Properties of simulated annealing search One can prove: If T decreases slowly enough, then simulated annealing search will find a global optimum with probability approaching 1Widely used in VLSI layout, airline scheduling, etc
44 Local beam search Keep track of k states rather than just one Start with k randomly generated statesAt each iteration, all the successors of all k states are generatedIf any one is a goal state, stop; else select the k best successors from the complete list and repeat.
45 Genetic algorithmsA successor state is generated by combining two parent statesStart with k randomly generated states (population)A state is represented as a string over a finite alphabet (often a string of 0s and 1s)Evaluation function (fitness function). Higher values for better states.Produce the next generation of states by selection, crossover, and mutation
46 Genetic algorithmsFitness function: number of non-attacking pairs of queens (min = 0, max = 8 × 7/2 = 28)24/( ) = 31%23/( ) = 29% etc
48 Online SearchingAn online search agent operates by interleaving computation and action. First, it takes action, then it observes the environment and computes the next action.However, there is a penalty for sitting around and computing too long. This is why we need efficient code.Usually used in exploration situations.
49 Offline Search vs. Online Search Knows the “map” of the situationBasically finds the shortest path knowing the whole layout of the situationWorks like a GPS navigation systemOnline Search:Doesn’t know the “map” of the situationHas to explore and find out where to go, then determine the shortest path
50 Search PatternsOften, online search agents search using a depth-first search patternThe search pattern must include whether or not the state space is safely explorable.Random walk method works, but not very well. It takes exponentially many steps to find the goal.Hill climbing search is by default an online search method, but only keeps one state in memory and can’t go back. Depth-first is more efficient.