# CS 151: Intelligent Agents, Problem Formulation and Search

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2 CS 151: Intelligent Agents, Problem Formulation and Search

3 How do we make a vacuum "smart?" Vacuum, clean the house! Um OK?? This one's got no chance

4 How do we represent this task? Hmmm where to begin? We want the vacuum to "clean the house." What does this mean? What's involved for the vacuum cleaner? How can we formalize this problem a bit?

5 Vacuum Cleaner World Percepts: location and contents, e.g., [A,Dirty] Actions: Left, Right, Suck, NoOp

6 Rational Agents Rational Agent = An agent (program) that does the "right" thing, given its goals, its abilities, what it perceives of its environment and its prior knowledge What do we need to know in order to decide if the vacuum cleaner is rational?

7 What does the agent DO? Our goal as AI programmers is develop agents that behave rationally. This means we must specify what the agent does given: Its goals Its precepts (what is perceives) Its possible actions Its prior knowledge This plan of action is the agent's policy or agent function AI Programming = Policy Design and Implementation

8 Defining a Policy How might we define a policy for the vacuum agent above if the goal is to clean both rooms?

9 Techniques for Implementing Policies Search Reasoning with knowledge and uncertainty Reasoning with Utility At the core of this class will be several techniques for Policy design and implementation Learning

10 Task Environments Deterministic, fully observable single-state problem Agent knows exactly which state it will be in; solution is a sequence of actions Non-observable sensorless (conformant) problem Agent may have no idea where it is; solution is a sequence Nondeterministic and/or partially observable contingency problem percepts provide new information about current state often interleave search, execution Unknown state space exploration problem

11 Example: vacuum world Single-state, start in #5.

12 Example: vacuum world Sensorless, start in {1,2,3,4,5,6,7,8} e.g., Right goes to {2,4,6,8}

13 Example: Vacuum world Contingency Nondeterministic: Suck may dirty a clean carpet Partially observable: location, dirt at current location. Percept: [L, Clean], i.e., start in #5 or #7

14 Vacuum world Search-based agent 1. Formulate problem and goal 2. Search for a sequence of actions that will lead to the goal (the policy) 3. Execute the actions one at a time

15 Vacuum world Search-based agent 1. Formulate problem and goal 2. Search for a sequence of actions that will lead to the goal (the policy) 3. Execute the actions one at a time Well-defined problem: (State space) Initial state Goal test Actions/Successor function Path cost

16 Vacuum world States: Shown above Actions: R, L, S, NoOp Successor function given by state graph (next slide) Goal test: In state 7 or 8? Path cost: +1 for each move and each suck

17 Vacuum world state space graph

18 Some example problems Toy problems and microworlds 8-Puzzle Missionaries and Cannibals Water Jug Problem Roomers NQueens Real-world problems

19 Another problem: 8-Puzzle

20 8-puzzle goal states? actions? path cost?

21 8-Puzzle state: all 3 x 3 configurations of the tiles on the board actions: Move Blank Square Left, Right, Up or Down. This is a more efficient encoding than moving each of the 8 distinct tiles path cost: +1 for each action

22 The 8-Queens Problem State transition:? Initial State:? Actions:? Goal: Place eight queens on a chessboard such that no queen attacks any other!

23 Missionaries and Cannibals Three missionaries and three cannibals wish to cross the river. They have a small boat that will carry up to two people. Everyone can navigate the boat. If at any time the Cannibals outnumber the Missionaries on either bank of the river, they will eat the Missionaries. Find the smallest number of crossings that will allow everyone to cross the river safely.

24 Water Jug Problem Given a full 5-gallon jug and a full 2-gallon jug, fill the 2-gallon jug with exactly one gallon of water. 5 2

25 Roomers: Baker, Cooper, Fletcher, Miller, and Smith live on different floors of an apartment house that contains only five floors. Baker does not live on the top floor. Cooper does not live on the bottom floor. Fletcher does not live on either the top or the bottom floor. Miller lives on a higher floor than does Cooper. Smith does not live on a floor adjacent to Fletcher s. Fletcher does not " live on a floor adjacent to Cooper s. Where does everyone live? " " [Taken verbatim from Abelson and Sussman, Structure and Interpretation of " Computer Programs, second edition, M.I.T. Press, The authors attribute " the puzzle to Dinesman, Superior Mathematical Puzzles, Simon and Schuster, " 1968.] "

26 Some real-world problems Route finding directions, maps computer networks airline travel VLSI layout Touring (traveling salesman) Agent planning

27 Search-based agent 1. Formulate problem and goal 2. Search for a sequence of actions that will lead to the goal (the policy) 3. Execute the actions one at a time

28 Search algorithms We ve defined the problem Now we want to find the solution! Use search techniques offline, simulated exploration of state space by generating successors of already-explored states (a.k.a. expanding states) Start at the initial state and search for a goal state What are candidate search techniques? BFS DFS

29 Finding the path: Tree search algorithms Basic idea: offline, simulated exploration of state space by generating successors of already-explored states (a.k.a. expanding states) def TreeSearch(problem, strategy): initialize search tree using information in the problem while true: if there are no candidates for expansion, return failure choose a leaf node for expansion according to strategy if node contains goal state, return solution else expand node and add resulting nodes to search tree

30 Careful! 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.

31 Implementation: general tree search

32 Tree Search Algorithm 1. Add the initial state (root) to the <fringe> 2. Choose a node (curr) to examine from the <fringe> (if there is nothing in <fringe> - FAILURE) 3. Is curr a goal state? If so, SOLUTION If not, continue 4. Expand curr by applying all possible actions (add the new resulting states to the <fringe>) 5. Go to step 2

33 Search strategies A search strategy is defined by picking the order of node expansion How to evaluate a strategy?

34 Uninformed search strategies Uninformed search strategies use only the information available in the problem definition Depth First Search Breadth First Search

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

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

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

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

39 Depth-first search Expand deepest unexpanded node Implementation: fringe = LIFO queue, i.e., put successors at front

40 Depth-first search Expand deepest unexpanded node Implementation: fringe = LIFO queue, i.e., put successors at front

41 Depth-first search Expand deepest unexpanded node Implementation: fringe = LIFO queue, i.e., put successors at front

42 Depth-first search Expand deepest unexpanded node Implementation: fringe = LIFO queue, i.e., put successors at front

43 Depth-first search Expand deepest unexpanded node Implementation: fringe = LIFO queue, i.e., put successors at front

44 Depth-first search Expand deepest unexpanded node Implementation: fringe = LIFO queue, i.e., put successors at front

45 Depth-first search Expand deepest unexpanded node Implementation: fringe = LIFO queue, i.e., put successors at front

46 Depth-first search Expand deepest unexpanded node Implementation: fringe = LIFO queue, i.e., put successors at front

47 Depth-first search Expand deepest unexpanded node Implementation: fringe = LIFO queue, i.e., put successors at front

48 Depth-first search Expand deepest unexpanded node Implementation: fringe = LIFO queue, i.e., put successors at front

49 Depth-first search Expand deepest unexpanded node Implementation: fringe = LIFO queue, i.e., put successors at front

50 Depth-first search Expand deepest unexpanded node Implementation: fringe = LIFO queue, i.e., put successors at front

51 Search algorithm properties Time (using Big-O) approximate the number of nodes generated (not necessarily examined) Space (using Big-O) the max # of nodes stored in memory at any time Complete If a solution exists, will we find it? Optimal If we return a solution, will it be the best/optimal (really just shallowest) solution

52 Activity Analyze DFS and BFS according to the criteria time, space, completeness and optimality (for time and space, analyze in terms of b, d, and m (max depth); for complete and optimal - simply YES or NO) Which strategy would you use and why? Brainstorm improvements to DFS and BFS

53 BFS Time: O(b d+1 ) Space: O(b d+1 ) Complete = YES Optimal = YES

54 Time and Memory requirements for BFS Depth Nodes Time Memory sec 1 MB 4 111, sec 106 MB min 10 GB hours 1 terabyte days 101 terabytes years 10 petabytes ,523 years 1 exabyte BFS with b=10, 10,000 nodes/sec; 10 bytes/node

55 DFS Time: O(b m ) Space: O(bm) Complete = YES, if space is finite (and no circular paths), NO otherwise Optimal = NO

56 Problems with BFS and DFS BFS memory! DFS Not optimal And not even necessarily complete!

57 Ideas? Can we combined the optimality and completeness of BFS with the memory of DFS? + =

58 Depth limited DFS DFS, but with a depth limit L specified nodes at depth L are treated as if they have no successors we only search down to depth L Time? O(b^L) Space? O(bL) Complete? No, if solution is longer than L Optimal No, for same reasons DFS isn t

59 Ideas?

60 Iterative deepening search For depth 0, 1,., run depth limited DFS if solution found, return result Blends the benefits of BFS and DFS searches in a similar order to BFS but has the memory requirements of DFS Will find the solution when L is the depth of the shallowest goal

61 Iterative deepening search L =0

62 Iterative deepening search L =1

63 Iterative deepening search L =2

64 Iterative deepening search L =3

65 Time? L = 0: 1 L = 1: 1 + b L = 2: 1 + b + b 2 L = 3: 1 + b + b 2 + b 3 L = d: 1 + b + b 2 + b b d Overall: d(1) + (d-1)b + (d-2)b 2 + (d-3)b b d O(b d ) the cost of the repeat of the lower levels is subsumed by the cost at the highest level

66 Properties of iterative deepening search Space? O(bd) Complete? Yes Optimal? Yes

67 Missionaries and Cannibals Solution Near side Far side! 0 Initial setup: MMMCCC B -! 1 Two cannibals cross over: MMMC B CC! 2 One comes back: MMMCC B C! 3 Two cannibals go over again: MMM B CCC! 4 One comes back: MMMC B CC! 5 Two missionaries cross: MC B MMCC! 6 A missionary & cannibal return: MMCC B MC! 7 Two missionaries cross again: CC B MMMC! 8 A cannibal returns: CCC B MMM! 9 Two cannibals cross: C B MMMCC! 10 One returns: CC B MMMC! 11 And brings over the third: - B MMMCCC!

68 Water Jug Problem Operator table 5 2 State = (x,y), where x is the number of gallons of water in the 5-gallon jug and y is # of gallons in the 2-gallon jug Initial State = (5,2) Goal State = (*,1), where * means any amount Name Cond. Transition Effect Empty5 (x,y) (0,y) Empty 5-gal. jug Empty2 (x,y) (x,0) Empty 2-gal. jug 2to5 x 3 (x,2) (x+2,0) Pour 2-gal. into 5-gal. 5to2 x 2 (x,0) (x-2,2) Pour 5-gal. into 2-gal. 5to2part y < 2 (1,y) (0,y+1) Pour partial 5-gal. into 2- gal.

69 The 8-Queens Problem State transition:? Initial State:? Actions:? Goal: Place eight queens on a chessboard such that no queen attacks any other!

70 Roomers: Baker, Cooper, Fletcher, Miller, and Smith live on different floors of an apartment house that contains only five floors. Baker does not live on the top floor. Cooper does not live on the bottom floor. Fletcher does not live on either the top or the bottom floor. Miller lives on a higher floor than does Cooper. Smith does not live on a floor adjacent to Fletcher s. Fletcher does not " live on a floor adjacent to Cooper s. Where does everyone live? " " [Taken verbatim from Abelson and Sussman, Structure and Interpretation of " Computer Programs, second edition, M.I.T. Press, The authors attribute " the puzzle to Dinesman, Superior Mathematical Puzzles, Simon and Schuster, " 1968.] "

71 Breadth-first search of the 8-puzzle - # of node denotes order in which the nodes are visited.

72 Depth-first search of the 8-puzzle with a depth bound of 5 (order on node denotes the order in which the nodes are examined - this pic is missing some nodes that were added but not examined).

73 Two heuristics applied to states in the 8-puzzle.

74 The heuristic f applied to states in the 8-puzzle.

75 State space generated in heuristic search of the 8- puzzle graph.

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