Lecture 2-State Space Search

Question 1

Why use State Space?

Answer 1

-Many AI problems can be expressed in terms of going from an initial state to a goal state

Question 2

Brute force search vs heuristic search

Answer 2

Brute force search:
-generate and search all possibilities but INEFFICIENT
Heuristic search:
-only try the possibilities that you think are more likely to lead to good solutions

Question 3

What is State Space represented by?

Answer 3

1.Inital state
2.Set of operations : responsible for transition between states
3.Goal test function: applied to a state to determine if it is a goal state
4.Path cost function: assigns a cost to a path to tell if a path is preferable to another

Question 4

What is search space?

Answer 4

-The set of all states that can be reached from the initial state by any sequence of action

Question 5

What is search algorithm?

Answer 5

-How the search space is visited

Question 6

What can prevent termination?

Answer 6

-Cycles in graph representation(blind search without cycle check may never terminate)

Question 7

What is uninformed search?

Answer 7

-We systematically explore the alternatives
-BRUTE FORCE SEARCH
-breadth-first, depth-first,depth-limited,iterative deepening,uniform cost

Question 8

Depth-first vs breadth-first

Answer 8

Determine order for examining states:
-Depth-first: visit successors before siblings
-Breadth-first: visit siblings before successors, level-by-level

Question 9

DFS vs BFS

Answer 9

-Only differ in the way they order nodes in the OPEN LIST
BFS :
-optimal (will always find shortest path)
-high memory
DFS:
-not optimal
-less memory
*BOTH IMPRACTICAL IN REAL APPLICATIONS BCUZ SEARCH SPACE TOO LARGE

Question 10

What is DFS?

Answer 10

-Uses a stack –> nodes are added on the TOP of the list(LIFO)

Question 11

What is BFS?

Answer 11

-Uses a queue –> nodes are added at the END of the list (FIFO)

Question 12

What is Depth-limited search(DLS)?

Answer 12

-Compromise for DFS : DFS +depth cutoff k

Question 13

What are the 3 possible outcomes of DLS?

Answer 13

-Solution
-Failure(no solution)
-Cutoff(no solution within cutoff)

Question 14

What is Iterative Deepening?

Answer 14

-Compromise between BFS and DFS : DFS + a max depth before going to next level
-Needs little memory
-Finds shortest path

Question 15

When is iterative deepening preferred?

Answer 15

When there is a large search space and the depth of the solution is unknown.

Question 16

What is uniform cost search?

Answer 16

-Uses a priority queue sorted using the cost from the root to node n
-Guarantees to find the lowest cost solution path

Question 17

What is informed search?

Answer 17

-We try to choose smartly
-HEURISTIC SEARCH
-best-first,…

Question 18

What is heuristic search(2)?

Answer 18

-A technique that improves the efficiency of search
-Focus on paths most promising

Question 19

What is the heuristic function h(n)?

Answer 19

-Estimate of the lowest cost from node n to the goal.

Question 20

What is the heuristic function g(n)?

Answer 20

-Cost of current path from start to node n

Question 21

What is the heuristic function f(n)?

Answer 21

-Estimate of the lowest cost of the solution path(from start to goal passing through n)

Question 22

What are the 2 types of hill climbing?

Answer 22

1.General hill climbing
2.Steepest ascent hill climbing

Question 23

What is general hill climbing strategy(2)?

Answer 23

-As soon as you find a position that is better than the current one, take it.
-Does not maintain a list of next nodes to visit(open list)

Question 24

What is steepest ascent hill climbing(1)?

Answer 24

-Pick the best position out of all the next possible moves

Question 25

What are the 2 problems with hill climbing?

Answer 25

1.Foothills(local maxima) 2.Plateau

Question 26

What are foothills(local maxima)(3)?

Answer 26

-Reached a local maximum, not the global maximum -A state that is better than all its neighbors but not better than some other states farther away. -All moves appear to make things worse

Question 27

What is plateau(2)?

Answer 27

-A flat area of search space in which the next states have the same value. -Not possible to determine best direction in which to move by making local comparisons.

Question 28

What are 2 solutions to hill-climbing problems?

Answer 28

1.Random-restart hill-climbing 2.Keep going even if the best successor has the same value as the current node (could lead to infinite loop on plateau)

Question 29

Why not to use hill-climbing?

Answer 29

-One move is selected and all others are forgotten Solution: use open as a priority queue AKA BEST-FIRST SEARCH

Question 30

What is best-first search(3)?

Answer 30

-Insert nodes in OPEN list so that the nodes are sorted in ascending h(n) -Always choose the next node to visit to be the one with the best h(n) -- regardless of where it is in the search space -It is an exhaustive search--> will eventually try all possible paths

Question 31

How to design heuristics(3)?

Answer 31

-Heuristic evaluation functions are highly dependent on the search domain -The more INFORMED , the BETTER on the SEARCH PERFORMANCE -Bad heuristics lead to frequent backtracking

Question 32

What are the 3 criteria in evaluating heuristics?

Answer 32

1.Admissibility 2.Monotonicity 3.Informedness

Question 33

What is admissibility(3)?

Answer 33

-Admissible if it never overestimates the cost of reaching the goal -Guarantees to find lowest cost SOLUTION PATH to the goals if it exists -Does not guarantee to find the lowest cost SEARCH PATH

Question 34

What is monotonicity(2)?

Answer 34

-Monotonic if it always finds the optimal path to each state the 1st time it is encountered -Guarantees to find lowest COST PATH to each state n encountered in the search

Question 35

What is informedness?

Answer 35

-More informed heuristics search smaller space to find the solution path

Question 36

Why not use heuristics?

Answer 36

-Might be wrong, so search could continue down a wrong path Solution: maintain depth/cost count (give preference to shorter/least expensive paths

Question 37

Why was A* search created?

Answer 37

-To help Shakey the robot navigate a room of obstacles

Question 38

What is the modified function f in A* search?

Answer 38

f(n) = g(n) + h(n) -f(n) estimate of total cost along path through n - g(n) actual cost of path from start to node n - h(n) estimate of cost to reach goal from node n

Question 39

What do you need in all search strategies?

Answer 39

1.Open list--> the frontier: -lists generated nodes not yet expanded -order of nodes controls order of search 2.Closed list-->the explored set -stores all the nodes that have already been visited TO AVOID CYCLES

Question 40

In data structures, what does each node in the lists contain?

Answer 40

-State representation -Parent-node -Children -Bookkeeping