1. Chapter 4 Search in State Spaces Xiu-jun GONG (Ph. D) School of Computer Science and Technology, Tianjin University gongxj@tju.edu.cn http://cs.tju.edu.cn/faculties/gongxj/course/ai/

2. Outline  Formulating the state space of a problem  Strategies for State Space Search Uninformed search: BFS,DFS Heuristic Search: A*, Hill-climbing Adversarial Search: MiniMax  Summary

3. State Space  a state space is a description of a configuration of discrete states used as a simple model of machines/problems. Formally, it can be defined as a tuple [N, A, S, G] where: N is a set of statesset A is a set of arcs connecting the states S is a nonempty subset of N that contains start statessubset G is a nonempty subset of N that contains the goal states.

4. State space search  State space search is a process used in the field of AI in which successive configurations or states of an instance are considered, with the goal of finding a goal state with a desired property. State space is implicit the typical state space graph is much too large to generate and store in memory nodes are generated as they are explored, and typically discarded thereafter A solution to an instance may consist of the goal state itself, or of a path from some initial state to the goal state.

5. Formulating the State Space  Explicit State Space Graph List all possible states and their transformation  Implicit State Space Graph State space is described by only essential states and their transformation rules

6. Explicit State Space Graph  8-puzzle problem state description  3-by-3 array: each cell contains one of 1-8 or blank symbol two state transition descriptions  84 moves: one of 1-8 numbers moves up, down, right, or left  4 moves: one black symbol moves up, down, right, or left

7. Explicit State Space Graph cont. Part of State space graph for 8-puzzle The number of nodes in the state-space graph: 9! ( = 362,880 ) Only small problem can be described by Explicit State-Space Graph

8. Implicit State Space Graph  Essential States start: (2, 8, 3, 1, 6, 4, 7, 0, 5)  Goals (1, 2, 3, 8, 0, 4, 7, 6, 5)  Rules 2 8 3 1 6 4 7 5 1 2 3 8 4 7 6 5

9. Implicit State Space Graph cont.  Rules R1: if A(I,J)=0 and J>0 then A(I,J):=A(I,J-1), A(I,J-1):=0 ( 空格左移 ) R2: if A(I,J)=0 and I>0 then A(I,J):=A(I-1,J), A(I-1,J):=0 ( 空格上移 ) R3: if A(I,J)=0 and J<2 then A(I,J):=A(I,J+1), A(I,J+1):=0 ( 空格右移 ) R4: if A(I,J)=0 and I<2 then A(I,J):=A(I+1,J), A(I+1,J):=0 ( 空格下移 ) J 0 1 2 0 1 2 I A(0,0)A(0,1)A(0,2) A(1,0)A(1,1)A(1,2) A(2,0)A(2,1)A(2,2) Implicit State-Space Graph (rules & essential states) uses less memory than Explicit State-Space Graph

10. Search strategy  For huge search space we need, Careful formulation Implicit representation of large search graphs Efficient search method  Uninformed Search Breadth-first search Depth-first search  Heuristic Search

11. Breadth-first search  Advantage Finds the path of minimal length to the goal.  Disadvantage Requires the generation and storage of a tree whose size is exponential the depth of the shallowest goal node  Extension Branch & bound

12. Depth-first search

13. DFS  Advantage Low memory size: linear in the depth bound  saves only that part of the search tree consisting of the path currently being explored plus traces  Disadvantage No guarantee for the minimal state length to goal state The possibility of having to explore a large part of the search space

14. Iterative Deepening  Simply put an upper limit on the depth (cost) of paths allowed  Motivation: e.g. inherent limit on range of a vehicle  tell me all the places I can reach on 10 litres of petrol prevents search diving into deep solutions might already have a solution of known depth (cost), but are looking for a shallower (cheaper) one

15. Iterative Deepening cont  Memory Usage Same as DFS O(bd)  Time Usage: Worse than BFS because nodes at each level will be expanded again at each later level BUT often is not much worse because almost all the effort is at the last level anyway, because trees are “leaf –heavy”

16. Heuristic Search  Using Evaluation Functions  A General Graph-Searching Algorithm  Algorithm A* Algorithm description Admissibility Consistence

17. Using Evaluation Functions  Best-first search (BFS) = Heuristic search proceeds preferentially using heuristics Basic idea  Heuristic evaluation function : based on information specific to the problem domain  Expand next that node, n, having the smallest value of  Terminate when the node to be expanded next is a goal node  Eight-puzzle The number of tiles out of places: measure of the goodness of a state description

18. Using Evaluation Functions cont. A Possible Result of a Heuristic Search Procedure

19. Using Evaluation Functions cont.  A refine evaluation function

20. A General Graph-Searching Algorithm 1. Create a search tree, Tr, with the start node n 0  put n 0 on ordered list OPEN 2. Create empty list CLOSED 3. If OPEN is empty, exit with failure 4. Select the first node n on OPEN  remove it  put it on CLOSED 5. If n is a goal node, exit successfully: obtain solution by tracing a path backward along the arcs from n to n 0 in Tr 6. Expand n, generating a set M of successors + install M as successors of n by creating arcs from n to each member of M 7. Reorder the list OPEN: by arbitrary scheme or heuristic merit 8. Go to step 3

21. A General Graph-Searching Algorithm cont.  Breadth-first search New nodes are put at the end of OPEN (FIFO) Nodes are not reordered  Depth-first search New nodes are put at the beginning of OPEN (LIFO)  Best-first (heuristic) search OPEN is reordered according to the heuristic merit of the nodes A* is an example of BFS  The algorithm was first described in 1968 by Peter Hart, Nils Nilsson, and Bertram Raphael.

22. Algorithm A *  Reorders the nodes on OPEN according to increasing values of  Some additional notation h(n): the actual cost of the minimal cost path between n and a goal node g(n): the cost of a minimal cost path from n 0 to n f(n) = g(n) + h(n): the cost of a minimal cost path from n 0 to a goal node over all paths via node n f(n 0 ) = h(n 0 ): the cost of a minimal cost path from n 0 to a goal node estimate of h(n)

23. Algorithm A * Cont. (Procedures) 1. Create a search graph, G, consisting solely of the start node, n 0  put n 0 on a list OPEN 2. Create a list CLOSED: initially empty 3. If OPEN is empty, exit with failure 4. Select the first node on OPEN  remove it from OPEN  put it on CLOSED: node n 5. If n is a goal node, exit successfully: obtain solution by tracing a path along the pointers from n to n 0 in G 6. Expand node n, generating the set, M, of its successors that are not already ancestors of n in G  install these members of M as successors of n in G

24. Algorithm A * Cont. (Procedures) 7. Establish a pointer to n from each of those members of M that were not already in G  add these members of M to OPEN  for each member, m, redirect its pointer to n if the best path to m found so far is through n  for each member of M already on CLOSED, redirect the pointers of each of its descendants in G 8. Reorder the list OPEN in order of increasing values 9. Go to step 3

26. Properties of A* Algorithm  Admissibility A heuristic is said to be admissible  if it is no more than the lowest-cost path to the goal.  if it never overestimates the cost of reaching the goal.  An admissible heuristic is also known as an optimistic heuristic.  A* is guaranteed to find an optimal path to the goal with the following conditions: Each node in the graph has a finite number of successors All arcs in the graph have costs greater than some positive amount  For all nodes in the search graph,

27. Properties of A* Algorithm cont.  The Consistency (or Monotone) condition Estimator h holds monotone condition, if (n j is a successor of n i )  A type of triangle inequality

28. Properties of A* Algorithm cont.  Consistency condition implies values of the nodes are monotonically nondecreasing as we move away from the start node  Theorem: If on is satisfied with the consistency condition, then when A* expands a node n, it has already found an optimal path to n

29. Relationships Among Search Algorithm

30. Adversarial Search, Game Playing  Two-Agent Games Idealized Setting: The actions of the agents are interleaved.  Example Grid-Space World Two robots : “Black” and “White” Goal of Robots  White : to be in the same cell with Black  Black : to prevent this from happening After settling on a first move, the agent makes the move, senses what the other agent does, and then repeats the planning process in sense/plan/act fashion.

31. Two-Agent Games: example

32. MiniMax Procedure (1)  Two player : MAX and MIN  Task : find a “best” move for MAX  Assume that MAX moves first, and that the two players move alternately.  MAX node nodes at even-numbered depths correspond to positions in which it is MAX’s move next  MIN node nodes at odd-numbered depths correspond to positions in which it is MIN’s move next

33. MiniMax Procedure (2)  Complete search of most game graphs is impossible. For Chess, 10 40 nodes  10 22 centuries to generate the complete search graph, assuming that a successor could be generated in 1/3 of a nanosecond  The universe is estimated to be on the order of 10 8 centuries old. Heuristic search techniques do not reduce the effective branching factor sufficiently to be of much help.  Can use either breadth-first, depth-first, or heuristic methods, except that the termination conditions must be modified.

34. MiniMax Procedure (3)  Estimate of the best-first move apply a static evaluation function to the leaf nodes measure the “worth” of the leaf nodes. The measurement is based on various features thought to influence this worth. Usually, analyze game trees to adopt the convention  game positions favorable to MAX cause the evaluation function to have a positive value  positions favorable to MIN cause the evaluation function to have negative value  Values near zero correspond to game positions not particularly favorable to either MAX or MIN.

35. MiniMax Procedure (4)  Good first move extracted Assume that MAX were to choose among the tip nodes of a search tree, he would prefer that node having the largest evaluation.  The backed-up value of a MAX node parent of MIN tip nodes is equal to the maximum of the static evaluations of the tip nodes. MIN would choose that node having the smallest evaluation.

36. MiniMax Procedure (5)  After the parents of all tip nods have been assigned backed-up values, we back up values another level. MAX would choose that successor MIN node with the largest backed-up value MIN would choose that successor MAX node with the smallest backed-up value. Continue to back up values, level by level from the leaves, until the successors of the start node are assigned backed-up values.

37. Tic-tac-toe example

38. The Improving of Adversarial Search  Waiting for a stable situation  Assistant search  Using knowledge  Taking a risk

39. Summary  How to formulate the state space of a problem  Strategies for State Space Search Uninformed search: BFS,DFS Heuristic Search: A*, Hill-climbing Adversarial Search: MiniMax

40. 1,3,0,0,0,0,0, 0,0,0 1,4,0,0,0,0,0, 0,0,0 2,4,0,0,0,0,0, 0,0,0 3,4,0,0,0,0,0, 0,0,0 2,3,0,0,0,0,0, 0,0,0