CS50 AI with Python Lecture 0
questions fully solved & updated
2025
Search - answer Finding a solution to a problem, like a navigator
app that finds the best route from your origin to the destination, or
like playing a game and figuring out the next move.
Knowledge - answer Representing information and drawing
inferences from it.
Uncertainty - answer Dealing with uncertain events using
probability.
Optimization - answer Finding not only a correct way to solve a
problem, but a better—or the best—way to solve it.
Learning - answer Improving performance based on access to data
and experience. For example, your email is able to distinguish spam
from non-spam mail based on past experience.
Neural Networks - answer A program structure inspired by the
human brain that is able to perform tasks effectively.
Language - answer Processing natural language, which is produced
and understood by humans.
Agent - answer An entity that perceives its environment and acts
upon that environment. In a navigator app, for example, the agent
would be a representation of a car that needs to decide on which
actions to take to arrive at the destination.
, State - answer A configuration of an agent in its environment. For
example, in a 15 puzzle, a state is any one way that all the numbers
are arranged on the board.
Initial State - answer The state from which the search algorithm
starts. In a navigator app, that would be the current location.
Actions - answer Choices that can be made in a state. More
precisely, actions can be defined as a function. Upon receiving state
s as input, Actions(s) returns as output the set of actions that can
be executed in state s. For example, in a 15 puzzle, the actions of a
given state are the ways you can slide squares in the current
configuration (4 if the empty square is in the middle, 3 if next to a
side, 2 if in the corner).
Transition Model - answer A description of what state results from
performing any applicable action in any state. More precisely, the
transition model can be defined as a function. Upon receiving state
s and action a as input, Results(s, a) returns the state resulting
from performing action a in state s. For example, given a certain
configuration of a 15 puzzle (state s), moving a square in any
direction (action a) will bring to a new configuration of the puzzle
(the new state).
State Space - answer The set of all states reachable from the initial
state by any sequence of actions. For example, in a 15 puzzle, the
state space consists of all the 16!/2 configurations on the board that
can be reached from any initial state. The state space can be
visualized as a directed graph with states, represented as nodes,
and actions, represented as arrows between nodes.
Goal Test - answer The condition that determines whether a given
state is a goal state. For example, in a navigator app, the goal test
would be whether the current location of the agent (the
representation of the car) is at the destination. If it is — problem
solved. If it's not — we continue searching.
questions fully solved & updated
2025
Search - answer Finding a solution to a problem, like a navigator
app that finds the best route from your origin to the destination, or
like playing a game and figuring out the next move.
Knowledge - answer Representing information and drawing
inferences from it.
Uncertainty - answer Dealing with uncertain events using
probability.
Optimization - answer Finding not only a correct way to solve a
problem, but a better—or the best—way to solve it.
Learning - answer Improving performance based on access to data
and experience. For example, your email is able to distinguish spam
from non-spam mail based on past experience.
Neural Networks - answer A program structure inspired by the
human brain that is able to perform tasks effectively.
Language - answer Processing natural language, which is produced
and understood by humans.
Agent - answer An entity that perceives its environment and acts
upon that environment. In a navigator app, for example, the agent
would be a representation of a car that needs to decide on which
actions to take to arrive at the destination.
, State - answer A configuration of an agent in its environment. For
example, in a 15 puzzle, a state is any one way that all the numbers
are arranged on the board.
Initial State - answer The state from which the search algorithm
starts. In a navigator app, that would be the current location.
Actions - answer Choices that can be made in a state. More
precisely, actions can be defined as a function. Upon receiving state
s as input, Actions(s) returns as output the set of actions that can
be executed in state s. For example, in a 15 puzzle, the actions of a
given state are the ways you can slide squares in the current
configuration (4 if the empty square is in the middle, 3 if next to a
side, 2 if in the corner).
Transition Model - answer A description of what state results from
performing any applicable action in any state. More precisely, the
transition model can be defined as a function. Upon receiving state
s and action a as input, Results(s, a) returns the state resulting
from performing action a in state s. For example, given a certain
configuration of a 15 puzzle (state s), moving a square in any
direction (action a) will bring to a new configuration of the puzzle
(the new state).
State Space - answer The set of all states reachable from the initial
state by any sequence of actions. For example, in a 15 puzzle, the
state space consists of all the 16!/2 configurations on the board that
can be reached from any initial state. The state space can be
visualized as a directed graph with states, represented as nodes,
and actions, represented as arrows between nodes.
Goal Test - answer The condition that determines whether a given
state is a goal state. For example, in a navigator app, the goal test
would be whether the current location of the agent (the
representation of the car) is at the destination. If it is — problem
solved. If it's not — we continue searching.
