Artificial Intelligence Exam 1 – Foundations of AI Concepts and
Introductory Study Guide
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: studying for an exam.
thinking rationally - ✔✔creating provable correct systems.
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
Introductory Study Guide
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: studying for an exam.
thinking rationally - ✔✔creating provable correct systems.
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
