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CSC325 Artificial Intelligence > Simple Search > Flashcards

Simple Search Flashcards

1
Q

What are the problem types for agents?

A
  • Deterministic
  • Non-observable
  • Nondeterministic and/or partially observable
  • Unknown state space
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2
Q

What is a deterministic problem type for agents?

A

Singe-state problem. Agent knowns exactly which state it will be in; solution is a sequence (fully observable)

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3
Q

What is a non-observable problem type?

A

Conformant problem. Agent may have no idea where it is; solution (if any) is a sequence

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4
Q

What is a nondeterministic and/or partially observable problem type?

A

Contingency problem. Percepts provide new information about current state. Solution is a contingent plan or a policy. Often interleaves search, execution.

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5
Q

What is an unknown state space problem type?

A

Exploration problem

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6
Q

What is the single-state problem formula?

A

Problem is defined by four items:
- initial state
- successor function (action-state pairs)
- Goal test
- Path cost
A solution is a sequence of actions leading from the initial state to a goal state.

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7
Q

Why must we abstract the real world?

A

The real world is absurdly complex therefore state space must be abstracted for problem solving.

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8
Q

What is a state (states vs nodes implementation)?

A

(representation of) A physical configuration

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9
Q

What is a node (states vs nodes implementation)?

A

A data structure constituting part of a search tree and includes parent, children, depth, path cost.

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10
Q

What are the factors we use to measure problem-solving performance?

A
  • Completeness
  • Optimality
  • Time Complexity
  • Space complexity
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11
Q

What is completeness when measuring problem-solving performance?

A

Is the algorithm guaranteed to find a solution when there is one?

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12
Q

What is optimality when measuring problem-solving performance?

A

Does the strategy find the/an optimal solution?

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13
Q

What is time complexity when measuring problem-solving performance?

A

How long does does it take to find a solution?

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14
Q

What is space complexity when measuring problem-solving performance?

A

How much memory is needed to perform the search?

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15
Q

How does breadth-first search work?

A

Expanded the shallowest unexpanded node first (row by row).

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16
Q

What is time and space complexity measured in terms of?

A

b - maximum branching factor of the search tree
d - depth of the least-cost solution
m - maximum depth of the state space (can be infinite)

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17
Q

What is a search strategy?

A

Picking the order of node expansion

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18
Q

What are uniformed search strategies?

A

Use only the information available in the problem definition (no-domain knowledge)

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19
Q

Does breadth-first search satisfy the completeness property?

A

Yes (if b, branching factor is finite)

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20
Q

What is the time complexity of breadth-first search?

A

O(b^(d+1))

21
Q

What is the space complexity of breadth-first search?

A

O(b^(d+1))

22
Q

Is breadth-first search optimal?

A

Yes if cost = 1 per step, otherwise not optimal in general.

23
Q

Is breadth-first search practical?

A

Not very practical unless solution is an a small tree.

24
Q

What is uniform-cost search?

A

Expand least-cost unexpanded node.

25
What is the implementation of uniform-cost search?
Create a fringe which is a queue ordered by path cost, lowest first. Same as breadth-first if steps costs all equal.
26
Does uniform-cost search show completeness?
Yes if the cost is within the minimum cost value
27
What is the time complexity of uniform cost search
O(b^[C*/e]), C* is the cost of optimal solution. E is minimum cost Number of nodes with g (path cost) <= cost of optimal solution
28
What is the space complexity of uniform-cost search?
O(b^[C*/e]), C* is the cost of optimal solution. E is minimum cost Number of nodes with g <= cost of optimal solution
29
Is uniform-cost search optimal?
Yes, nodes are expanded in increasing order of g(n)
30
What is depth-first serch?
Expand deepest unexpanded child node. Once full depth has been expanded discard searched side.
31
Does depth-first search show completeness?
No- fails in infinite-depth spaces or spaces with loops.
32
What is the time complexity of depth-first search?
O(b^m) - terrible if m is much larger than d, but for dense solutions faster than breath-first
33
What is the space complexity of depth-first search?
O(bm), linear space
34
Is depth-first search optimal?
No - we may end up in infinite paths.
35
What is a depth-limited search?
Do a depth-first search until limit x has been reached.
36
What is an Iterative Deepening search?
Combination of depth-first search's space-efficiency and breadth-first search's fast search. Every call is a DFS with a restricted depth.
37
Does Iterative Deepening search show completeness?
Yes
38
What is the time complexity of a iterative deepening search?
O(b^d)
39
What is the space complexity of an iterative deepening search?
O(bd)
40
Is iterative deepening search optimal?
Yes, if step cost = 1
41
Which simple searches are complete and not complete?
Complete - Breadth-first (finite trees) - Uniform cost (if step cost >= e) - Depth-first (finite spaces) - Iterative deepening Not complete: - Breadth-first (infinite trees) - Depth-first (inifinite and loops) - Depth-limited search -
42
Which simple searches algorithms are optimal and not optimal?
Optimal: - Breadth-First (only if step cost = 1) - Uniform-cost (nodes expanded in increasing order) - Iterative deepening (if step cost = 1) Not optimal: - Breadth-First (if step cost > 1) - Depth-First - Depth-limited
43
What is the implementation of a depth-first search?
Using a fringe, LIFO queue
44
How do dynamic and BDI agents operate?
Dynamic and BDI agents work of a reasoning cycle. - They perceive the environment - Calculate the goal from the current state - Determine what rules/actions to take to reach that goal - Execute the actions
45
What is the graph search infrastructure (for nodes)
- n.STATE - n.PARENT - n.ACTION - n.PATH-COST
46
What is the n.STATE (graph search infrastructure)?
The current state is the state space that the current node corresponds to
47
What is the n.PARENT (graph search infrastructure)?
The previous node in the state space that generated the current node.
48
What is the n.ACTION (graph search infrastructure)?
The action of the previous node (PARENT) that generated the current node
49
What is the n.PATH-COST (graph search infrastructure)?
The cost, denoted by g(n), of path from the initial state to the current node

CSC325 Artificial Intelligence (8 decks)

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