CS 171 Artificial Intelligence (Russell & Norvig)

Study of systems that:- think like humans- act like humans- think rationally - act rationallu

Test for intelligent behaviorSystem providing answer passes the test if interrogator can't tell whether the answers come from a person or not

System that can:Formulate a theory of mind/brainExpress the theory in a computer program

Approach to creating a system that thinks like a human by testing or predicting the response of human subjects

Approach to creating a system that thinks like a human by observing neurological data

System that can solve problems using "laws of thought" (syllogisms, notation and logic, etc.)

Ideal intelligence (in contrast with human intelligence)

System that carries out actions to achieve the best outcome

Anything that perceives and acts on its environment

Study of rational agents

Rational agents carry out an action with the best outcome after considering past and current percepts (T/F)

p = current percepta = action carried outF = agent function

Function that maps from percept histories to actionsf = P* ? A

Agent = architecture + program (T/F)

P in PEASCaptures agent's aspiration

E in PEASContext, restrictions

A in PEASIndicates what the agent can carry out

S in PEASIndicates what the agent can perceive

Environment where everything an agent requires to choose its actions is available to it via its sensorsvs. Partially observable

Environment that is predictable, follows a sequenceEx. In a sequence of 1,2,3,4,5, 5 happened because of 1-4vs. Stochastic

Environment where events that occur now may be direct consequence of past events

Environment where choice of current action is not dependent on previous actionsvs.

Sequential

Environment where all previous choices are taken into account. Current choose will affect future actionsvs. Episodic

Environment that does not changevs. Dynamic

Environment that changesvs. Static

Environment where past events do not affect what happens nextvs. Continuous

Environment that is like a sequential environmentvs.

Discrete

Agent operating by itself VS multiagent - many agents working together

Agent given percept and stimulus, will respond (given A, will give B)

Reflex agent knows its state

Agent that has a goal and makes choices to improve its state

Agent that also considers a "happiness factor" aside from goal state

Agent with a performance element. Its learning element modifies performance element