Week 1 (23)

Question 1

agent

Answer 1

-Perceives environment through sensors
-acts on environment through actuators

Question 2

Percept

Answer 2

Agents perceptual input at any given instant

Question 3

Percept sequence

Answer 3

Complete history of everything the agent has ever perceived

Question 4

Agent function

Answer 4

Describes an agents behaviour

Maps any given Percept sequence to an action

Question 5

Agent program

Answer 5

Concrete implementation of an agent function running within some physical system

(Check if this is exact as textbook says ‘it is important to keep these two ideas distinct’)

Question 6

Rational agent

Answer 6

For each possible Percept sequence,

a rational agent should select an action that is expected to maximise its performance measure,

given the evidence provided by the Percept sequence and whatever built in knowledge the agent has

Question 7

Performance measure

Answer 7

Something that evaluate any given sequence of environment states (not agent states)

Question 8

4 factors that determine rationality

Answer 8

Performance measure defining criterion of success

Agents prior knowledge of env

Actions that agent can perform

Agents Percept sequence to date

Question 9

Omniscience

Answer 9

Agent knows actual outcome of actions and can act accordingly

Question 10

Information gathering

Answer 10

Doing actions in order to modify future Percepts

Question 11

Exploration

Answer 11

A type of information gathering in which the search space is inspected

Question 12

Autonomy

Answer 12

Relying on own precepts over prior knowledge

Question 13

Task environment

Answer 13

PEAS

Performance

Environment

Actuators

Sensors

Question 14

Softbots

Answer 14

Software agents

Software robots

Question 15

Fully observable v partial observable

Answer 15

If an agents sensors give it access to the complete state of the env at each point in time

Question 16

Example of emergent behaviour in competitive environment

Answer 16

Randomised behaviour (to avoid being predictable)

Question 17

Example of emergent behaviour in cooperative environment

Answer 17

Communication

Question 18

Deterministic env

Answer 18

If next state of env is completely determined by the current state and the action executed by the agent

Question 19

When can an environment appear to be stochastic

Answer 19

When it is partially observable

Question 20

Uncertain environment

Answer 20

Not fully observable

Not deterministic

Question 21

Distinction between non deterministic environment and stochastic environment

Answer 21

Non determinism: actions are characterised by possible outcomes

Stochastic: same as above + probabilities associated to possible outcomes

Question 22

Episodic env v sequential env

Answer 22

Episodic: the actions taken in a previous episode do not effect subsequent episodes

Episode: agent receives a Percept then performs a single action

Question 23

Discrete env v Continuous env

Answer 23

Refers to handling of time

Discrete set of percepts and actions

Question 24

Known env v unknown env

Answer 24

Known: outcomes for all actions are given

Unknown: agent must learn how env works to make good decisions

Question 25

Example of distinction between (un)known and partially/fully observable

Answer 25

Solitaire:

Rules known

Exact cards not yet turned over

Question 26

Environment generator

Answer 26

Selects particular environments from environment class with given likelihoods to evaluate agent

Question 27

Agent =

Answer 27

Architecture + program

Question 28

Difference between agent function and agent program inputs

Answer 28

Program takes just current Percept as input

Function taken entire Percept history

Question 29

Simple reflex agent

Answer 29

Selection actions based on current Percept ignoring history

Question 30

How can a simple reflex agent escape a loop

Answer 30

If the agent can randomise actions

Question 31

Internal state

Answer 31

For model based reflex agent

Depends on Percept history and reflects the data that an agent maintains internally of environment

Question 32

2 pieces of info needed by model based agent

Answer 32

Information about how world evolves independent of agent

Information about how agents actions effect world

Question 33

Utility function

Answer 33

An agents internalisation of the performance measure

Question 34

Difference between performance measure and utility function

Answer 34

Performance measure is objective measurement of environment not done by agent

Question 35

Rational utility based agent chooses the action that maximises

Answer 35

EXPECTED utility