Module 1 & 2

Question 1

What is AI?

Answer 1

System that acts rationally, thinks like humans, thinks rationally, acts like humans

Question 2

What’s an Agent

Answer 2

Entity that perceives and acts

Question 3

What’s a rational agent

Answer 3

Agent that selects action to maximize utility or value of performance measure

Question 4

List characteristics of an agent and environment

Answer 4

Agent perceives through environment and actuators help agent act in the environment

Question 5

What’s an agent function

Answer 5

Maps percept to the action, takes in current percept to determine action

Question 6

What’s agent function dependent on

Answer 6

Machine and agent problem

Question 7

Can all agent functions be implemented by an agent problem?

Answer 7

No

Question 8

What does rational depend on

Answer 8

Performance measure, Agents prior knowledge, Agents actions, percept history

Question 9

What does performance measure evaluate

Answer 9

Environment sequence

Question 10

Can Rational agents explore and learn? Autonomous? Omniscient?

Answer 10

Autonomous, not omniscient, may explore and learn

Question 11

What is the task environment

Answer 11

Performance measure, Environment, Actions, Sensors (PEAS)

Question 12

Environment types

Answer 12

Ok

Question 13

Fully observable vs partially

Answer 13

Full observable - state has all info of environment relevant to task

Partially observable - does not have all info. Agent needs memory

Question 14

Single Agent vs Multi agent

Answer 14

How many agents in env, how do their actions affect us?

Multi agent - agent may behave randomly

Question 15

Deterministic vs stochastic domain

Answer 15

Deterministic - resulting state depends on action and state. Certainty

Stochastic - uncertainty in the resulting state, probability involved. Agent needs contingencies

Question 16

Episodic domain vs Sequential domain

Answer 16

Episodic - actions are independent of previous actions. No consequence

Sequential - current choice will affect future actions, long term consequences

Question 17

Static vs dynamic domain

Answer 17

Static - environment doesn’t change agent has time for decision

Dynamic - environment changes

Question 18

Discrete vs continuous

Answer 18

Discrete - limited number of distinct and defined states , precepts, actions and steps .

Otherwise continuous - agent needs operating controller

Question 19

Agent Types

Answer 19

Ok

Question 20

Simple reflex

Answer 20

Selects actions on basis of current percept, ignores percept history.

Environment needs to be full observable

Question 21

Reflex agents with states

Answer 21

Keeps memory state of percept history

Internal state holds transition model and sensor model.

Question 22

What is transition model

Answer 22

How the world works

Question 23

What is sensor model

Answer 23

How state of world is reflected in agents percept

Question 24

Goal based agents and pro / con

Answer 24

Agents require goal info describing desirable situations
Pro - knowledge that supports decision is explicit
Con - cant handle trade off or uncertainty / probability

Question 25

Utility based agents

Answer 25

Goals are not enough since they can be non optimal
Uses utility function to determine best action
Pro - computes expected value for actions and handles uncertainty
Con - cant easily index into actions

Question 26

What is atomic spectrum

Answer 26

Each state is indivisible

Question 27

What is factored spectrum

Answer 27

Splits state into fixed set of variables or attributes with their own value

Question 28

What is structured spectrum

Answer 28

Objects and their various relationships can be described explicitly