# State Spaces Graph Search Searching. Graph Search. CPSC 322 Search 2. Textbook 3.4. Graph Search CPSC 322 Search 2, Slide 1

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1 Graph Search CPSC 322 Search 2 Textbook 3.4 Graph Search CPSC 322 Search 2, Slide 1

2 Lecture Overview 1 State Spaces 2 Graph Search 3 Searching Graph Search CPSC 322 Search 2, Slide 2

3 State Spaces Idea: sometimes it doesn t matter what sequence of observations brought the world to a particular configuration; it just matters how the world is arranged now. called the Markov assumption Represent the different configurations in which the world can be arranged as different states which numbers are written in cells of the Sudoku and which are blank? which numbers appear in which slots of the 8-puzzle? where is the delivery robot? States are assignments of values to one or more variables a single variable called state x and y coordinates; etc... From each state, one or more actions may be available, which would move the world into a new state write a new number in a blank cell of the Sudoku slide a tile in the 8-puzzle move the delivery robot to an adjacent location Graph Search CPSC 322 Search 2, Slide 3

4 Agent Design An agent can be thought of as a mapping from the given state to the new action that the agent will take However, there s a problem... often, we don t understand the domain well enough to build the mapping we d need to be able to tell the agent how it should behave in every state that s why we want intelligent agents: they should decide how to act for themselves in order for them to do so, we need to give them goals Graph Search CPSC 322 Search 2, Slide 4

5 State Spaces Represent the different configurations in which the world can be arranged as different states which numbers are written in cells of the Sudoku and which are blank? which numbers appear in which slots of the 8-puzzle? where is the delivery robot? States are assignments of values to one or more variables From each state, one or more actions may be available, which would move the world into a new state write a new number in a blank cell of the Sudoku slide a tile in the 8-puzzle move the delivery robot to an adjacent location Some states are goal states A Sudoku state in which all numbers are different in each box, row and column The single 8-puzzle state pictured earlier The state in which the delivery robot is located in room 123 Graph Search CPSC 322 Search 2, Slide 5

6 Lecture Overview 1 State Spaces 2 Graph Search 3 Searching Graph Search CPSC 322 Search 2, Slide 6

7 Search What we want to be able to do: find a solution when we are not given an algorithm to solve a problem, but only a specification of what a solution looks like idea: search for a solution Definition (search problem) A search problem is defined by A set of states A start state A goal state or goal test a boolean function which tells us whether a given state is a goal state A successor function a mapping from a state to a set of new states Graph Search CPSC 322 Search 2, Slide 7

8 Abstract Definition How to search Start at the start state Consider the different states that could be encountered by moving from a state that has been previously expanded Stop when a goal state is encountered To make this more formal, we ll need to talk about graphs... Graph Search CPSC 322 Search 2, Slide 8

9 Search Graphs Definition (graph) A graph consists of a set N of nodes; a set A of ordered pairs of nodes, called arcs or edges. Node n 2 is a neighbor of n 1 if there is an arc from n 1 to n 2. i.e., if n 1, n 2 A Definition (path) A path is a sequence of nodes n 0, n 1,..., n k such that n i 1, n i A. Definition (solution) Given a start node and a set of goal nodes, a solution is a path from the start node to a goal node. Graph Search CPSC 322 Search 2, Slide 9

10 Example Domain for the Delivery Robot The agent starts outside room 103, and wants to end up inside room 123. r131 r129 r127 r125 r123 r121 r119 storage o131 o129 o127 o125 o123 o121 o119 l4d1 l4d2 l3d1 o117 r117 l4d3 l3d2 l3d3 o115 r115 l1d1 l2d1 l2d2 l1d2 l1d3 l2d3 l2d4 o113 r113 mail ts o101 o103 o105 o107 o109 o111 stairs r101 r103 r105 r107 r109 r111 Graph Search CPSC 322 Search 2, Slide 10

11 Example Graph for the Delivery Robot r123 storage o125 o123 o119 l3d1 l3d2 l3d3 l2d1 l2d2 l2d3 l2d4 mail ts o103 o109 o111 Graph Search CPSC 322 Search 2, Slide 11

12 Example Sudoku Problem Fives Alive Sudoku Grid.jpg (JPEG Image, 981x980 pixels) - Scaled (97%) Let s define this as a search problem. What are: the set of states? 1 of 1 9/16/09 3:22 PM Graph Search CPSC 322 Search 2, Slide 12

13 Example Sudoku Problem Fives Alive Sudoku Grid.jpg (JPEG Image, 981x980 pixels) - Scaled (97%) Let s define this as a search problem. What are: the set of states? the start state? 1 of 1 9/16/09 3:22 PM Graph Search CPSC 322 Search 2, Slide 12

14 Example Sudoku Problem Fives Alive Sudoku Grid.jpg (JPEG Image, 981x980 pixels) - Scaled (97%) Let s define this as a search problem. What are: the set of states? the start state? the goal state or goal test? 1 of 1 9/16/09 3:22 PM Graph Search CPSC 322 Search 2, Slide 12

15 Example Sudoku Problem Fives Alive Sudoku Grid.jpg (JPEG Image, 981x980 pixels) - Scaled (97%) Let s define this as a search problem. What are: the set of states? the start state? the goal state or goal test? the successor function? 1 of 1 9/16/09 3:22 PM Graph Search CPSC 322 Search 2, Slide 12

16 Example Sudoku Problem Fives Alive Sudoku Grid.jpg (JPEG Image, 981x980 pixels) - Scaled (97%) Let s define this as a search problem. What are: the set of states? 1 of 1 9/16/09 3:22 PM the start state? the goal state or goal test? the successor function? Note: here only the goal matters, not the path to it. Graph Search CPSC 322 Search 2, Slide 12

17 Graph Searching Generic search algorithm: given a graph, start nodes, and goal nodes, incrementally explore paths from the start nodes. Maintain a frontier of paths from the start node that have been explored. As search proceeds, the frontier expands into the unexplored nodes until a goal node is encountered. Graph Search CPSC 322 Search 2, Slide 13

18 Problem Solving by Graph Searching start node frontier explored nodes unexplored nodes Graph Search CPSC 322 Search 2, Slide 14

19 Graph Searching Generic search algorithm: given a graph, start nodes, and goal nodes, incrementally explore paths from the start nodes. Maintain a frontier of paths from the start node that have been explored. As search proceeds, the frontier expands into the unexplored nodes until a goal node is encountered. The way in which the frontier is expanded defines the search strategy. Graph Search CPSC 322 Search 2, Slide 15

20 Lecture Overview 1 State Spaces 2 Graph Search 3 Searching Graph Search CPSC 322 Search 2, Slide 16

21 Graph Search Algorithm Input: a graph, a set of start nodes, Boolean procedure goal(n) that tests if n is a goal node. frontier := { s : s is a start node}; while frontier is not empty: select and remove path n 0,..., n k from frontier; if goal(n k ) return n 0,..., n k ; for every neighbor n of n k add n 0,..., n k, n to frontier; end while After the algorithm returns, it can be asked for more answers and the procedure continues. Which value is selected from the frontier defines the search strategy. The neighbor relationship defines the graph. The goal function defines what is a solution. Graph Search CPSC 322 Search 2, Slide 17

22 Branching Factor Definition (forward branching factor) The forward branching factor of a node is the number of arcs going out of that node. If the forward branching factor of every node is b and the graph is a tree, how many nodes are exactly n steps away from the start node? Graph Search CPSC 322 Search 2, Slide 18

23 Branching Factor Definition (forward branching factor) The forward branching factor of a node is the number of arcs going out of that node. If the forward branching factor of every node is b and the graph is a tree, how many nodes are exactly n steps away from the start node? b n nodes. We ll assume that all branching factors are finite. Graph Search CPSC 322 Search 2, Slide 18

24 Comparing Algorithms Definition (complete) A search algorithm is complete if, whenever at least one solution exists, the algorithm is guaranteed to find a solution within a finite amount of time Definition (time complexity) The time complexity of a search algorithm is an expression for the worst-case amount of time it will take to run, expressed in terms of the maximum path length m and the maximum branching factor b. Definition (space complexity) The space complexity of a search algorithm is an expression for the worst-case amount of memory that the algorithm will use, expressed in terms of m and b. Graph Search CPSC 322 Search 2, Slide 19

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