Problem solving, search and control strategies

Transcription

1 Materials Ch 2 & 3 of Artificial Intelligence A Systems Approach by Tim Jones Chapters 3 and 4 of Artificial Intelligence a Modern Approach by Russell and Norvig

2 Problem solving, search and control strategies

3 GENERAL PROBLEM SOLVING

4 GENERAL PROBLEM SOLVING

5 GENERAL PROBLEM SOLVING

6 GENERAL PROBLEM SOLVING

7 AI: SEARCH AND CONTROL STRATEGIES

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11 Tree search algorithms Basic idea: offline, simulated exploration of state space by generating successors of already-explored states (a.k.a.~expanding states) 14 Jan 2004 CS Blind Search 11

12 Tree search example 14 Jan 2004 CS Blind Search 12

13 Tree search example 14 Jan 2004 CS Blind Search 13

14 Tree search example 14 Jan 2004 CS Blind Search 14

15 Implementation: general tree search 14 Jan 2004 CS Blind Search 15

16 Implementation: states vs. nodes A state is a (representation of) a physical configuration A node is a data structure constituting part of a search tree includes state, parent node, action, path cost g(x), depth The Expand function creates new nodes, filling in the various fields and using the SuccessorFn of the problem to create the corresponding states. 14 Jan 2004 CS Blind Search 16

17 Search strategies A search strategy is defined by picking the order of node expansion Strategies are evaluated along the following dimensions: completeness: does it always find a solution if one exists? time complexity: number of nodes generated space complexity: maximum number of nodes in memory optimality: does it always find a least-cost solution? Time and space complexity are measured in terms of b: maximum branching factor of the search tree d: depth of the least-cost solution m: maximum depth of the state space (may be )

18 Uninformed search strategies Uninformed search strategies use only the information available in the problem definition Breadth-first search Depth-first search Depth-limited search Iterative deepening search

19 Breadth-first search Expand shallowest unexpanded node Implementation: fringe is a FIFO queue, i.e., new successors go at end

20 Breadth-first search Expand shallowest unexpanded node Implementation: fringe is a FIFO queue, i.e., new successors go at end 14 Jan 2004 CS Blind Search 20

21 Breadth-first search Expand shallowest unexpanded node Implementation: fringe is a FIFO queue, i.e., new successors go at end 14 Jan 2004 CS Blind Search 21

22 Breadth-first search Expand shallowest unexpanded node Implementation: fringe is a FIFO queue, i.e., new successors go at end 14 Jan 2004 CS Blind Search 22

23 Properties of breadth-first search Complete? Yes (if b is finite) Time? 1+b+b 2 +b 3 + +b d + b(b d -1) = O(b d+1 ) Space? O(b d+1 ) (keeps every node in memory) Optimal? Yes (if cost = 1 per step) Space is the bigger problem (more than time) 14 Jan 2004 CS Blind Search 23

24 Depth-first search Expand deepest unexpanded node Implementation: fringe = LIFO queue, i.e., put successors at front 14 Jan 2004 CS Blind Search 24

25 Depth-first search Expand deepest unexpanded node Implementation: fringe = LIFO queue, i.e., put successors at front 14 Jan 2004 CS Blind Search 25

26 Depth-first search Expand deepest unexpanded node Implementation: fringe = LIFO queue, i.e., put successors at front 14 Jan 2004 CS Blind Search 26

27 Depth-first search Expand deepest unexpanded node Implementation: fringe = LIFO queue, i.e., put successors at front 14 Jan 2004 CS Blind Search 27

28 Depth-first search Expand deepest unexpanded node Implementation: fringe = LIFO queue, i.e., put successors at front 14 Jan 2004 CS Blind Search 28

29 Depth-first search Expand deepest unexpanded node Implementation: fringe = LIFO queue, i.e., put successors at front 14 Jan 2004 CS Blind Search 29

30 Depth-first search Expand deepest unexpanded node Implementation: fringe = LIFO queue, i.e., put successors at front 14 Jan 2004 CS Blind Search 30

31 Depth-first search Expand deepest unexpanded node Implementation: fringe = LIFO queue, i.e., put successors at front 14 Jan 2004 CS Blind Search 31

32 Depth-first search Expand deepest unexpanded node Implementation: fringe = LIFO queue, i.e., put successors at front 14 Jan 2004 CS Blind Search 32

33 Depth-first search Expand deepest unexpanded node Implementation: fringe = LIFO queue, i.e., put successors at front 14 Jan 2004 CS Blind Search 33

34 Depth-first search Expand deepest unexpanded node Implementation: fringe = LIFO queue, i.e., put successors at front 14 Jan 2004 CS Blind Search 34

35 Depth-first search Expand deepest unexpanded node Implementation: fringe = LIFO queue, i.e., put successors at front 14 Jan 2004 CS Blind Search 35

36 Properties of depth-first search Complete? No: fails in infinite-depth spaces, spaces with loops Modify to avoid repeated states along path complete in finite spaces Time? O(b m ): terrible if m is much larger than d but if solutions are dense, may be much faster than breadth-first Space? O(bm), i.e., linear space! Optimal? No 14 Jan 2004 CS Blind Search 36

37 Depth Limited search Alleviating the embarrassing failure of depth-first search in infinite state spaces by supplying depth-first search with a predetermined depth-limit of l. Nodes at depth l are treated as if they have no successors. Unfortunately, it also introduces an additional source of incompleteness if we choose l<d It will also be non-optimal if we choose l>d ts time complexity is O(b l )and its space complexity is O(bl). Depth-first search can be viewed as a special case of depthlimited search with l=. depth-limited search has two modes of failure: standard failure - no solution. cutoff failure - no solution within the depth limit

38 Depth-limited search = depth-first search with depth limit l, i.e., nodes at depth l have no successors Recursive implementation:

39 Iterative deepening search general strategy, often used in combination with depth-first tree search, that finds the best depth limit. It does this by gradually increasing the limit - first 0, then 1, then 2, and so on - until a goal is found. This will occure when the depth reaches d, the depth of the shallowest goal node. The algorithm is shown in Figure DFS-15: Combines the benefits of depth-first search and breadth-first search. Memory requirements are: O(bd) N(IterativeDeepeningSearch)=(d)b + (d-1)b (1)b 2 This gives a time complexity of O(b d )

40 Iterative deepening search l =0 14 Jan 2004 CS Blind Search 40

41 Iterative deepening search l =1 14 Jan 2004 CS Blind Search 41

42 Iterative deepening search l =2 14 Jan 2004 CS Blind Search 42

43 Iterative deepening search l =3 14 Jan 2004 CS Blind Search 43

44 Iterative deepening search Number of nodes generated in a depth-limited search to depth d with branching factor b: N DLS = b 0 + b 1 + b b d-2 + b d-1 + b d Number of nodes generated in an iterative deepening search to depth d with branching factor b: N IDS = (d+1)b 0 + d b^1 + (d-1)b^ b d-2 +2b d-1 + 1b d For b = 10, d = 5, N DLS = , , ,000 = 111,111 N IDS = , , ,000 = 123,456 Overhead = (123, ,111)/111,111 = 11% 14 Jan 2004 CS Blind Search 44

45 Properties of iterative deepening search Complete? Yes Time? (d+1)b 0 + d b 1 + (d-1)b b d = O(b d ) Space? O(bd) Optimal? Yes, if step cost = 1 14 Jan 2004 CS Blind Search 45

46 Summary of algorithms 14 Jan 2004 CS Blind Search 46

47 Graph search 14 Jan 2004 CS Blind Search 47

48 Summary Problem formulation usually requires abstracting away realworld details to define a state space that can feasibly be explored Variety of uninformed search strategies Iterative deepening search uses only linear space and not much more time than other uninformed algorithms