APT3010 ARTIFICIAL INTELLIGENCE EXAM 1 PREP QUESTIONS WITH CORRECT ANSWERS; 100% SOLVED, GUARANTEED TO PASS: LATEST STUDY EXAM FOR THE AI
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This document contains a compilation of practice test for the ARTIFICIAL INTELLIGENCE board exam. This prep exam questions will improve your knowledge and understanding on ARTIFICIAL INTELLIGENCE topics.
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APT3010 ARTIFICIAL INTELLIGENCE
EXAM 1 PREP QUESTIONS WITH
CORRECT ANSWERS
acting humanly - can simulate and emulate humans, so it's more familiar
well known test is the Turing test
Turing test - A test proposed by Alan Turing in which a machine would be judged
"intelligent" if the software could use a chat conversation to fool a human into thinking it
was talking with a person instead of a machine.
thinking humanly - simulating and emulating the thought processes of humans.
Example: neural networks
acting rationally - doing the best / optimal action. Usually this is based on some sort of
objective function. If the objective function(s) is not aligned with human values, it might
not behave humanly.
Example: constraint satisfaction problems and expert systems. Problematic in the health
field due to a knowledge cliff.
Examples of AI Problems - Roomba
Spam filtering
Voice Assistants (like Siri)
Chess and board game players
agent - an agent is something that views its environment through sensors, and acts
upon the environment through actuators.
intelligent - intelligent agents are agents that behave rationally
precept - an agent's input at a given instance
precept sequence - a history of inputs that the agent has perceived
,agent function - a function that maps the percept sequence to the an agent's actions
agent program - the actual implementation internally of how the agent maps an percept
sequence to an action
rational agent - an agent that does the right thing for any particular percept sequence,
by maximizing a particular performance measure,
it's dependent on what given knowledge the agent has
omniscient agent - an agent that is all knowing
information gathering - a rational agent that doesn't have knowledge might have to
perform actions that modify future precepts. This is _
exploration - a type of information gathering in which an agent performs a series of
action to get information in a "partially-observable" environment
learning - after the information gathering, the agent needs to do this to process and
improve from what it perceives
autonomy - if the agent can learn and adapt on its own, it has this. Otherwise, the agent
behaves completely on prior knowledge and is very fragile
task environments - problems spaces for which agents are the solutions. Can be
specified through PEAS
PEAS stands for - Performance Criteria: how to evaluate how the agent behaves
Environment: everything that the agent perceives or acts upon
Actuators: components that the agent has to act upon the environment
Sensors: components that the agent has to sense the environment
Example of PEAS for Amazon recommendation engine - P: a count of how many
recommended products the customer actually buys
E: customers
A: a GUI that displays recommendations in a sorted order.
S: the number of buys, returns, comments that all of the customers
software agents - agents that exist only in the software world. Like the Amazon
recommendation engine
fully observable - an environment in which the agent knows the complete relevant state
of the environment at all times. No need for an internal state or exploration
, partially observable - might have noisy inaccurate sensors, or missing data. Like our
local Roomba robot.
unobservable - have absolutely no knowledge about the environment. Seemingly
impossible, but sometimes still able to solve the problem
single agent - only one agent in the environment (such as a crossword puzzle)
multiagent - more than one agent in the environment
ex: chess, or taxi driving
cooperative multiagent - In an environment, the other agents might have different
objective functions than the agent
ex: taxi driving
competitive multiagent - In an environment, the other agents have the same objective
function than the agent
ex: chess
randomized behavior - might be beneficial in competitive multi agent environments in
order to thwart predictability
deterministic - if the agent's actions have predictable effects. Ie, given a current state
and the agent's action, we could predict the next state
stochastic - the opposite of deterministic.
Ex: taxi driving. Might have erratic traffic, action might not lead to expected
consequences.
uncertain - either not fully observable OR not deterministic
nondeterministic - even more extreme than stochastic, because we do not know the
probability distributions of each possible outcome from an action
episodic - each sequence of action is independent from the others
ex: spotting defective parts in an assembly line
sequential - opposite of episodic
ex: chess and taxi driving
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