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Cram1 Flashcards

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1
Q

The agent perceives ______ acts according to a ______

A

environment, performance criteria

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2
Q

Performance criteria are

A

domain dependent

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3
Q

Possible performance criteria:

A

m2 per hour
how clean is the room after vacuuming
power consumption

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4
Q

Ideal rational agent

A

The agent chooses an action which maximizes its performance for a given
percept sequence and knowledge about the world.

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5
Q

to avoid trivialization use ____

A

active sensing

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6
Q

Categorization of Agents

A

Percepts: Symptoms, findings, patient’s answers,
Actions: Questions, tests, treatments,
Goals (performance measures): Healthy patient, minimize costs,
Environment: Patient, hospital

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7
Q

The goal of learning

A

Optimize future behavior on the basis of the history of percepts, actions, and knowledge about the world.

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8
Q

accessible vs. nonaccessible

A

Are all relevant aspects of the world accessible to the sensors?

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9
Q

deterministic vs. nondeterministic/stochastic

A

Does the next state depend completely on the current state and the action chosen.

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10
Q

episodic vs. nonepisodic

A

Does the choice of an action depend only on the current state or also on the past?

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11
Q

static vs. dynamic

A

Can the world change while deciding on the next action?

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12
Q

discrete vs. continuous

A

Is the world discrete (as in chess) or not (mobile robots)?

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13
Q

Properties of Environments

A

accessible, deterministic, episodic, static, discrete

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14
Q

A goal is

A

a set of world states, which the agent finds desirable (wants to reach one of them).

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15
Q

Search

A

Finding an action sequence which transforms an initial state into a goal state.

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16
Q

Single-state problem

A

complete world knowledge,

complete knowledge about the actions

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17
Q

Multiple-state problem

A

incomplete world knowledge,

complete knowledge about the actions

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18
Q

Contingency problem

A

incomplete knowledge about actions,

needs to gather information at run-time

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19
Q

Exploration problem

A

World states and effect of actions are

both unkown. Very difficult!

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20
Q

Initial state

A

World state which the agent believes to be in initially

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21
Q

State space

A

Set of all possible states

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22
Q

Operator

A

Description of which state is reached by an action from a given state.

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23
Q

Successor function S

A

S(x) returns the set of states reachable by any action from state x

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24
Q

Goal test

Study These Flashcards
A

Tests whether a state is a goal state.

25
Path
Sequence of actions
26
Path cost
Cost function over paths, usually the sum of the costs of the actions of the path, often denoted as g.
27
Solution
Path from the initial state to a state that satisfies the goal test
28
Search cost
Time and memory needed to find a solution
29
Total cost
Sum of the path cost (of the solution) and search cost
30
8-Puzzle
States: Describe the location of each tile including the blank. Operators: Blank moves left, right, up, or down. Goal test: Does the state match picture on the right? Path cost: Each step costs 1 unit. Search cost: Problem is NP-complete in the size of the puzzle.
31
Completeness
Does it always find a solution if one exists?
32
Time Complexity
How long does it take to find a solution in the worst case?
33
Space Complexity
How much memory is used in the worst case?
34
Optimality
Does it always find the best solution?
35
Uninformed (blind) search
No information available about the length or the cost of a solution. breadth-first search, uniform cost search, depth-first search
36
Informed (heuristic) search
has information about the length or the cost of a solution | Greedy search, A* and some variants, hill-climbing
37
Cost of Breadth-First Search
O(b^(d+1))
38
Iterative Deepening
Combines depth- and breadth-first search Time complexity: O(b^d) Space complexity: O(b*d)
39
Problems with Bidirectional Search
Operators not always reversible | Incompletely described goal states (e.g. checkmate)
40
A*
combines uniform cost search with greedy search g(n) = actual cost from the initial state to n. h(n) = estimated cost from n to the nearest goal f (n) := g(n) + h(n) f (n) = is the estimated cost of the cheapest path which passes through n.
41
h is called admissible if
h(n) <= h*(n) for all n
42
Path-Max Equation
n parent of n' | f (n') = max(f(n), g(n') + h(n'))
43
Games are a special variant of
search problems
44
The states are usually
accessible
45
Games belong to the class of
contingency problems (uncertain knowledge of action effects).
46
When the search space is large, the game tree can only be generated up to a certain depth. Minimax works then as well. Simply replace
TERMINAL-TEST by CUT-OFF-TEST and the utility function UTILITY by the evaluation function EVAL.
47
Evaluation Functions should be
easy to compute and accurately reflect the chance of winning
48
Complexity of Alpha-Beta
random leaves: O((b/logb)^d) best: O(b^(d/2)) usually: O(3d/4)
49
Knowledge Base (KB):
- contains sentences in a language with a truth theory (Logic) which we can interpret as propositions about the world - causal effect on the agent’s behavior in correlation with the contents of the sentences
50
Knowledge Level
what is known by the KB. E.g.: automatic taxi driver knows that Vaalser St connects Aachen and Vaals
51
Symbolic Level
Encoding of the KB as sentences in a formal language: Connects(Vaalser _St, Aachen, Vaals)
52
Implementation Level
The internal representation of sentences. Taxi-example: - a string “Connects(Vaalser_St, Aachen, Vaals)” - a bit in in a 3-D-matrix representing connections between places.
53
declarative language
System believes P iff P is thought to be true
54
precise language
- which strings are sentences; - what it means for a sentence to be true (without having to explicitly specify for every sentence whether or not it is true).
55
explicit knowledge
KB
56
implicit knowledge
{a | KB |= a}
57
Deductive inference
Process to compute the logical consequences of a KB. | given a KB and a query a, computes whether KB|=a.
58
Herbrand universe
the set of all terms which can be formed from the functions and terms in S

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