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COMP 472 > Lecture 2-State Space Search > Flashcards

Lecture 2-State Space Search Flashcards

1
Q

Why use State Space?

A

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

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2
Q

Brute force search vs heuristic search

A

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

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3
Q

What is State Space represented by?

A

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

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4
Q

What is search space?

A

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

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5
Q

What is search algorithm?

A

-How the search space is visited

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6
Q

What can prevent termination?

A

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

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7
Q

What is uninformed search?

A

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

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8
Q

Depth-first vs breadth-first

A

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

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9
Q

DFS vs BFS

A

-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

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10
Q

What is DFS?

A

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

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11
Q

What is BFS?

A

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

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12
Q

What is Depth-limited search(DLS)?

A

-Compromise for DFS : DFS +depth cutoff k

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13
Q

What are the 3 possible outcomes of DLS?

A

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

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14
Q

What is Iterative Deepening?

A

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

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15
Q

When is iterative deepening preferred?

A

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

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16
Q

What is uniform cost search?

A

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

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17
Q

What is informed search?

A

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

18
Q

What is heuristic search(2)?

A

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

19
Q

What is the heuristic function h(n)?

A

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

20
Q

What is the heuristic function g(n)?

A

-Cost of current path from start to node n

21
Q

What is the heuristic function f(n)?

A

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

22
Q

What are the 2 types of hill climbing?

A

1.General hill climbing
2.Steepest ascent hill climbing

23
Q

What is general hill climbing strategy(2)?

A

-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)

24
Q

What is steepest ascent hill climbing(1)?

A

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

25
Q

What are the 2 problems with hill climbing?

A

1.Foothills(local maxima)
2.Plateau

26
Q

What are foothills(local maxima)(3)?

A

-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

27
Q

What is plateau(2)?

A

-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.

28
Q

What are 2 solutions to hill-climbing problems?

A

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)

29
Q

Why not to use hill-climbing?

A

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

30
Q

What is best-first search(3)?

A

-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

31
Q

How to design heuristics(3)?

A

-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

32
Q

What are the 3 criteria in evaluating heuristics?

A

1.Admissibility
2.Monotonicity
3.Informedness

33
Q

What is admissibility(3)?

A

-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

34
Q

What is monotonicity(2)?

A

-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

35
Q

What is informedness?

A

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

36
Q

Why not use heuristics?

A

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

37
Q

Why was A* search created?

A

-To help Shakey the robot navigate a room of obstacles

38
Q

What is the modified function f in A* search?

A

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

39
Q

What do you need in all search strategies?

A

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

40
Q

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

A

-State representation
-Parent-node
-Children
-Bookkeeping

COMP 472 (9 decks)

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