2. Artificial Intelligence Methods Rao Vemuri Searching - 2

3. Problem Definition - 1 Initial State –The initial state of the problem, defined in some suitable manner Operator –A set of actions that moves the problem from one state to another

4. Problem Definition - 1 Neighbourhood (Successor Function) –The set of all possible states reachable from a given state State Space –The set of all states reachable from the initial state

5. Problem Definition - 2 Goal Test –A test applied to a state which returns if we have reached a state that solves the problem Path Cost –How much it costs to take a particular path

6. Problem Definition - Example 54 618 732 123 84 765 123 456 78 147 258 36 Initial StateGoal State

7. Problem Definition - Example States –A description of each of the eight tiles in each location that it can occupy. It is also useful to include the blank Operators –The blank moves left, right, up or down

8. Problem Definition - Example Goal Test –The current state matches a certain state (e.g. one of the ones shown on previous slide) Path Cost –Each move of the blank costs 1

9. Problem Definition - Datatype Datatype PROBLEM –Components INITIAL-STATE, OPERATORS, GOAL-TEST, PATH-COST-FUNCTION

10. How Good is a Solution? Does our search method actually find a solution? Is it a good solution? –Path Cost –Search Cost (Time and Memory) Does it find the optimal solution? –But what is optimal?

11. Evaluating a Search Completeness –Is the strategy guaranteed to find a solution? Time Complexity –How long does it take to find a solution?

12. Evaluating a Search Space Complexity –How much memory does it take to perform the search? Optimality –Does the strategy find the optimal solution where there are several solutions?

13. Search Trees x xxx x x x x x o x o x x o x ………..

14. Search Trees ISSUES –Search trees grow very quickly –The size of the search tree is governed by the branching factor –Even this simple game has a complete search tree of 984,410 potential nodes –The search tree for chess has a branching factor of about 35

15. Implementing a Search - What we need to store State –This represents the state in the state space to which this node corresponds Parent-Node –This points to the node that generated this node. In a data structure representing a tree it is usual to call this the parent node

16. Implementing a Search - What we need to store Operator –The operator that was applied to generate this node Depth –The number of nodes from the root (i.e. the depth) Path-Cost –The path cost from the initial state to this node

17. Implementing a Search - Datatype Datatype node –Components: STATE, PARENT-NODE, OPERATOR, DEPTH, PATH-COST

18. Using a Tree – The Obvious Solution? Advantages –It’s intuitive –Parent’s are automatically catered for

19. Using a Tree – The Obvious Solution? But –It can be wasteful on space –It can be difficult the implement, particularly if there are varying number of children (as in tic-tac-toe) –It is not always obvious which node to expand next. We may have to search the tree looking for the best leaf node (sometimes called the fringe or frontier nodes). This can obviously be computationally expensive

20. Using a Tree – Maybe not so obvious Therefore –It would be nice to have a “simpler” data structure to represent our tree –And it would be nice if the next node to be expanded was an O(1) operation

21. Basic Queue Operations Make-Queue(Elements) –Create a queue with the given elements Empty?(Queue) –Returns true if the queue is empty Remove-Front(Queue) –Removes the element at the head of the queue and returns it

22. Queue Operations - Adding Elements Queuing- FN(Elements,Queue) –Inserts a set of elements into the queue. Different queuing functions produce different search algorithms.

23. General Search Function GENERAL-SEARCH(problem, QUEUING-FN) returns a solution or failure –nodes = MAKE-QUEUE(MAKE-NODE(INITIAL-STATE[problem])) –Loop do If nodes is empty then return failure node = REMOVE-FRONT(nodes) If GOAL-TEST[problem] applied to STATE(node) succeeds then return node nodes = QUEUING-FN(nodes,EXPAND(node,OPERATORS[problem])) –End End Function

24. General Search Function GENERAL-SEARCH(problem, QUEUING-FN) returns a solution or failure –nodes = MAKE-QUEUE(MAKE- NODE(INITIAL-STATE[problem])) –Loop do If nodes is empty then return failure node = REMOVE-FRONT(nodes) If GOAL-TEST[problem] applied to STATE(node) succeeds then return node nodes = QUEUING-FN(nodes,EXPAND(node,OPERATORS[problem])) –End End Function

25. General Search Function GENERAL-SEARCH(problem, QUEUING-FN) returns a solution or failure –nodes = MAKE-QUEUE(MAKE-NODE(INITIAL-STATE[problem])) –Loop do If nodes is empty then return failure node = REMOVE-FRONT(nodes) If GOAL-TEST[problem] applied to STATE(node) succeeds then return node nodes = QUEUING-FN(nodes,EXPAND(node,OPERATORS[problem])) –End End Function

26. General Search Function GENERAL-SEARCH(problem, QUEUING-FN) returns a solution or failure –nodes = MAKE-QUEUE(MAKE-NODE(INITIAL-STATE[problem])) –Loop do If nodes is empty then return failure node = REMOVE-FRONT(nodes) If GOAL-TEST[problem] applied to STATE(node) succeeds then return node nodes = QUEUING-FN(nodes,EXPAND(node,OPERATORS[problem])) –End End Function

27. General Search Function GENERAL-SEARCH(problem, QUEUING-FN) returns a solution or failure –nodes = MAKE-QUEUE(MAKE-NODE(INITIAL-STATE[problem])) –Loop do If nodes is empty then return failure node = REMOVE-FRONT(nodes) If GOAL-TEST[problem] applied to STATE(node) succeeds then return node nodes = QUEUING-FN(nodes,EXPAND(node,OPERATORS[problem])) –End End Function

28. General Search Function GENERAL-SEARCH(problem, QUEUING-FN) returns a solution or failure –nodes = MAKE-QUEUE(MAKE-NODE(INITIAL-STATE[problem])) –Loop do If nodes is empty then return failure node = REMOVE-FRONT(nodes) If GOAL-TEST[problem] applied to STATE(node) succeeds then return node nodes = QUEUING-FN(nodes,EXPAND(node,OPERATORS[problem])) –End End Function

29. General Search Function GENERAL-SEARCH(problem, QUEUING-FN) returns a solution or failure –nodes = MAKE-QUEUE(MAKE-NODE(INITIAL-STATE[problem])) –Loop do If nodes is empty then return failure node = REMOVE-FRONT(nodes) If GOAL-TEST[problem] applied to STATE(node) succeeds then return node nodes = QUEUING- FN(nodes,EXPAND(node,OPERATOR S[problem])) –End End Function

30. Artificial Intelligence Methods Rao Vemuri End of Searching-2