Wissensverarbeitung. - Search - Alexander Felfernig und Gerald Steinbauer Institut für Softwaretechnologie Inffeldgasse 16b/2 A-8010 Graz Austria

Transcription

1 - Search - Alexander Felfernig und Gerald Steinbauer Institut für Softwaretechnologie Inffeldgasse 16b/2 A-8010 Graz Austria 1

2 References Skriptum (TU Wien, Institut für Informationssysteme, Thomas Eiter et al.) ÖH-Copyshop, Studienzentrum Stuart Russell und Peter Norvig. Artificial Intelligence - A Modern Approach. Prentice Hall Vorlesungsfolien TU Graz (partially based on the slides of TUWien) 2

3 Goals Uninformed Search Strategies. Informed Search Strategies. 3

4 Search Important method of Artificial Intelligence Approaches: Uninformed search Informed search (exploit information/heuristics about the problem structure) Example: Which is the shortest path from A to D? A B D C E 4

5 Elements of a Search Problem Start node (start state) Goal node (goal state) States are generated dynamically on the basis of generation rule R example: move one element to the empty field Search goal: sequence of rules that transform the start state to the goal state start state goal state

6 Search Problem Start state (S 0 ) Non-empty set of goal states (goal test) Non-empty set of operators: Transform state S i into state S i+1 Transformation triggers costs (>0) No cost estimation available default: unit costs Cost function for search paths: of individual transformations 6

7 Properties of Search Methods Completeness Does the method find a solution, if one exists? Space Complexity How do memory requirements increase with increasing sizes of search problems (search spaces)? Time Complexity What is the computing performance with increasing sizes of search problems (search spaces)? Optimality Does the method identify the optimal solution (if one exists)? 7

8 Breadth-First Search (bfs) Level-wise expansion of states Systematic strategy Expand all paths of length 1 Expand all paths of length 2 Completeness yes (if b is finite). Space complexity: branching factor b, depth of the shallowest solution d: b d+1 Time complexity: b d+1 Optimality yes (if step costs are identical). 8

9 bfs D G A C F B E [A] [B,C,D] [B,C,D] [C,D,E,F] [C,D,E,F] [D,E,F,G] [D,E,F,G] [E,F,G] [E,F,G] [F,G] [F,G] [G] [G] 9

10 Uniform Cost Search (ucs) A S A B C S B G S S 15 5 C A B 5 C 15 A B C 15 Goal: cheapest path from S to G (independent of # transitions) Approach: Expand S, then expand A (since A has the cheapest current path) Solution not necessarily found opt. could be < 11 Expand B finally solution identified with path costs = 10 G 11 A G

11 Uniform Cost Search (ucs) Generalization of bfs Expand state with lowest related costs There could be a solution with shorter path but higher costs Corresponds to bfs if step costs are identical Completeness yes (if b finite & step costs > ). Space complexity: branching factor b, cost of the optimal solution C*: bc*/ +1 Time complexity: bc*/ +1 Optimality yes. 11

12 Depth First Search (dfs) No level-wise expansion Expansion of successor nodes of one deepest state If no more states can be expanded backtracking Completeness no (indefinite descent paths) Space complexity: branching factor b, maximum depth of the search tree m: b m Time complexity: b m Optimality no. 12

13 dfs D G A C F B E H [A] [B,C,D] [B,C,D] [E,F,C,D] [E,F,C,D] [H,F,C,D] [H,F,C,D] [F,C,D] [F,C,D] [C,D] [C,D] [F,G,D] [F,G,D] [G,D] [G,D] 13

14 DF Iterative Deepening (dfid) Search depth defined by bound l If search depth l is reached backtracking If no solution has been found for level l l = l + 1 Example using a binary tree: Level 0: Level 1: Repeated dfs until search level l. Level 2: 14

15 DF Iterative Deepening Completeness yes (if b is finite). Space complexity: branching factor b, depth of the shallowest solution d: b d Time complexity: b d Optimality yes (if step costs are identical). 15

16 Comparison of Uninformed Search Strategies Criterion Breadth- First Uniform- Cost Depth- First Completeness Yes a Yes a,b No Yes a Time O(b d+1 ) O(b C*/ +1 ) O(b m ) O(b d ) Iterative Deepening Space O(b d+1 ) O(b C*/ +1 ) O(bm) O(bd) Optimality Yes c Yes No Yes c 16

17 Heuristic Search Informed search which exploits problem- and domain-specific knowledge Estimation function: estimates the costs from the current node to the goal state Heuristic approach denoted as best-first search, since best-evaluated state is chosen for expansion Used notation: f: estimation function; f * (the real function) h(n): estimator of minimal costs from n to a goal state Invariant for goal state S n : h(n) = 0 Methods to be discussed: Greedy search A* search 17

18 Greedy Search Estimates minimal costs from current to the goal node Does not take into account already existing costs, i.e., f(n) = h(n) Completeness no (loopcheck needed). Space complexity: branching factor b, maximum depth of the search tree m : b m Time complexity: b m Optimality no. 18

19 Structure of Working Example 19

20 Greedy Search straight-line distance h (sld) 20

21 Greedy Search straight-line distance h (sld) 21

22 Greedy Search straight-line distance h (sld) 22

23 Summarizing Greedy Search Use sld heuristics Choose shortest path from Arad to Bucharest Arad: expand lowest h-value Sibiu: expand lowest h-value Fagaras: expand lowest h-value Bucharest found path: Arad, Sibiu, Rimnicu, Pitesti, Bucharest length of solution: 418km (in contrast to solution found 450km) Greedy search is based on local information Better choice: take into account already existing efforts 23

24 Greedy Search: Loops 24

25 A* Search In contrast to greedy search, it takes into account already existing costs as well. g(n) = path costs from start to current node Results in a more fair selection strategy for expansion Estimation function: f(n) = g(n) + h(n); h(n) the same as for greedy search A*: best-first search method with the following properties: Finite number of possible expansions Transition costs are positive (min. ) admissible (h), i.e., h does never overestimate the real costs (it is an optimistic heuristic) Consequently: f never overestimates the real costs 25

26 A* Search 26

27 A* Search 27

28 A* Search 28

29 Summarizing A* Search Used functions g(n): real distance Arad-n h(n): sld n-bucharest Taking into account global information improves the quality of the identified solution Expansion ordering (lowest f-value) Sibiu, Rimnicu, Pitesti, Bucharest Optimal solution will be found 29

30 Determination of Heuristics Heuristic function h must be optimistic No formal rule set of determining heuristic functions exists, however, some rules of thumb are there Example (8-Puzzle) 2 heuristic functions h 1 and h 2 h 1 : number of squares in wrong position h 2 : sum(distances of fields from goal state) start state Simplification: do not take into account goal the state influences of moves to other fields. For start state, h 1 =7 and h 2 = 15 Dominance relationship: h 2 dominates h 1 since for each n: h 1 (n) h 2 (n) h 2 is assumed to be more efficient; h 1 has tendency towards breadth first search. 30

31 Properties of A* Search f-values along a path in the search tree are never decreasing (monotonicity property) theorem: monoton(f) h(n 1 ) c(n 1,n 2 ) + h(n 2 ) goal expansion of nodes with increasing f-value 31

32 Properties of A* Search Distance circles of 380, 400, 420km (f-values) Expand all n with f(n) 380, then 400, Let f* be the costs of an optimal solution: A* expands all n with f(n) < f*, some m with f(m) = f*, and the goal state will be identified Completeness yes (h is admissible). Space complexity: exponential Time complexity: exponential Optimality yes. 32

33 Optimality of A* Search Suppose suboptimal (path to) goal G 2 in the queue. Let n be an unexpanded node on a shortest path to an optimal goal G f(g 2 ) = g(g 2 ) since h(g 2 )=0 > g(g) since G 2 is suboptimal [f(g 2 ) > g(g)] >= f(n) since h is admissible [f(g 2 ) > f(n)] Since f(g 2 ) > f(n), A* will never select G 2 for expansion 33

34 Local Search Previously: systematic exploration of search space path to goal is solution to the problem However, for some problems the path is irrelevant for example, 8-queens problem state space = set of configurations (complete) goal: identify configuration which satisfies a set of constraints Other algorithms can be used local search algorithms try to improve the current state by generating a follow-up state (neighbor state) are not systematic, not optimal, and not complete 34

35 Local Search: Example n-queens problem: positioning of n queens in an nxn board s.t. the number of 2 queens positioned on the same column, row or diagonal is minimized or zero. 35

36 Hill Climbing Algorithm Finding the top of Mount Everest in a thick fog while suffering from amnesia [Russel & Norvig, 2003] Greedy local search 36

37 Hill Climbing: Local Maxima Peak that is higher than each of its neighboring states but lower than the global maximum. h = number of pairs of queens attacking each other successor function: move single queen to another square in the same column h = 1 local maximum (minimum): every move of a single queen makes the situation worse [Russel & Norvig 2003] 37

38 Hill Climbing: Ridges Sequence of local maxima. 38

39 Hill Climbing: Plateaux Evaluation function is flat, a local maximum or a shoulder from which progress is possible. 39

40 Simulated Annealing Hill climbing never selects downhill moves Danger of, for example, local maxima Instead of picking the best move, choose a random one and accept also worse solutions with a decreasing probability 40

41 Genetic Algorithms Start with k randomly generated states (the population) Each state (individual) represented by a string over a finite alphabet (e.g., 1..8) Each state is rated by evaluation (fitness) function (e.g., #non-attacking pairs of queens opt. = 28) Selection probability directly proportional to fitness score, for example, 23/( ) 29% 41

42 Genetic Algorithms: Cross-Over 42

43 Genetic Algorithm function GENETIC_ALGORITHM( population, FITNESS-FN) return an individual input: population, a set of individuals FITNESS-FN, a function which determines the quality of the individual repeat new_population empty set loop for i from 1 to SIZE(population) do x RANDOM_SELECTION(population, FITNESS_FN) y RANDOM_SELECTION(population, FITNESS_FN) child REPRODUCE(x,y) if (small random probability) then child MUTATE(child ) add child to new_population population new_population until some individual is fit enough or enough time has elapsed return the best individual 43

44 Exercise 1. Explain the major differences between informed and uninformed search when to use which approach? 2. Explain the major differences between local search algorithms and A*. 3. Which search approach would you use for solving the Travelling Salesman Problem? 4. Explain the term admissibility in the context of best first search. 5. Show incompleteness and non-optimality of depthfirst search on the basis of a simple example. 44

45 Thank You! 45