CS8691 - Artificial Intelligence.pdf

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TM

5th
TM

TM
[Actuator]
? thinks
[Environment]
[Percepts]
[Action]
[Sensor]
AI
Agent program
Design
Agent function
Implements
Perception
Maps
Action
Leads to
Agent program
architecture

TM
An agent program
(Agent function)
Input
(Current percepts
from sensors)
Output
(Action made
through actuators)

TM
Optimal / right action / behaviour
Result into desired
sequence of states
Perceive sequence which
generator optimal
sequence actions
Agent coupled
with complex
environment
Environment known
Good agent
Rational behaviour
Environment unknown
Bad agent
Irrational behaviour

TM
Sequence
of percept
Rationality
Depends
on
Maximizes
Maximizes
is not same as
Perfection
Actual
performance
Expected
performance

TM
Low
Rising order of
complexity
High

TM
Percept
through sensors
Belief
Goals
Desires
through actuators
Action
Obligation
Interaction
Knowledge
Intelligent
agent
Enviroment
(world with agents)
World
without
agent

TM
Intelligent agent
An entity
is
1. Perception
2. Action
which perform?

TM
What the
world is like
now?
What action
should do
now?
I
Condition - action rule
Actuator
Sensor
Environment

TM
What action
should do now?
I
Condition-action rule
Actuators
Sensor
Environment
State
What my action do?
What the world
is like now?
How the world
evolves?

TM
What action
should do now?
I
Goals
Actuators
Sensor
Environment
What may action do?
What it will be like
if do action A?
I
What the world
is like now?
How the world
evolves?
State

TM
Actuator
Environment
State
How the world evolves
What may actions do
Utilitity
What the
world is like
now?
What it will be
like if do
I
action A?
How happy will be
I
in such a state?
What action I
should do now?
Sensor

TM
Environment
Performance
standard
Feedback
Learning
Changes
Knowledge
Sensors
Actuators
Sensors
Performance
element
Critic
Learning
element
Goals
Problem
generator


TM

TM
Mumbai
Pune
Satara
Dharwad
Chitradurg
Kolhapur
Vijapur
Banglore
Krushagiri
Ongala
Hyderabad
Aurangabad
A. Nagar
Koppal
Chennai
Vijaywada
Beed
Puducherri
Vellore
Tirupati
Phaltan
Guti
Solapur
Kodappa
190
120
110
125
160
175
200
450
100
105
480
165
242
65
305
90
85
190
267
125
120
160
270
100
110
110
60
155
145
Madurai
150
Nellore
Osmanabad
150
160

c (x, a, y)

 
3 1014
TM

3 1014


3 1014



TM

TM
Problem
solving
agent
Sequence of
actions
Successful
states
Generate Act

TM
Problem
{ Set of goals }
Performance measure
Action, states
Goal satisfied
Goal information
Problem and search
formulation
Execution











TM


TM

 22 (Two possiblities)

TM

TM
RB
LB
RB
LB
RB
LB
LB
RB
RB
LB
P
P
P
P
P
P
P
P
LB
LB
RB
RB
RB
LB

8 8

64 63 57 3 1014
   
...
3 1014

3 3

TM
2 1 3
8 6 4
7 – 5
1 2 3
6 – 4
7 6 5
(a) nitial state
I (b) Goal state






TM


c a
i i
 ci
a i
TM

c a
c a
:
c a
:
c a
1 1
2 2
i i
m m




c a
1 1

TM




TM

CS8691 - Artificial Intelligence.pdf

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CS8691 - Artificial Intelligence.pdf