Knowledge & Knowledge Base and AI Agents

1. ARTIFICIAL
INTELLIGENCE
(CS511
)
Lectur
e
Instruct
or
4
Waqas Ahmad
Department
of
Computer
Science
University of Agriculture,
Faisalabad

2. Artificia
l
Intelligen
ce
 Artificial intelligenc
e
(AI
)
is intelligenc
e
demonstrate
d
by machine, as opposed to the natural intelligence
displayed
by humans or animals.

3. A FEW
DOMAINS
OF
AI
Formal
Tasks:
Medical
Diagnosis
Mundane Task:
Tic- Tac-
Toe
Chess
Checkers
Expert
Task:
Perception
Common
Sense
Robot
Control
Reasoni
ng
Engineering
Design
Fault
Finding
Scientific
Analysis

4. Knowledg
e
 One of the few hard and fast result to come out of the first
three
decades of AI research is that intelligence requires
Knowledge.
 properties of
knowledge:
 It
 It
 It
is
is
is
voluminous
hard to characterize
accurately
constantly changing

The
different kinds of
knowledge
thatnee
d
to be represente
d
in AI
syste
mObjects
Events
Performan
ce
Facts
Meta-
Knowledge
I.
II.
III.
IV.
V.
VI.

5. Types
of
knowledg
e
in AI
Declarative Knowledge – It includes
concepts, facts,
and
objects

expressed in a declarative
sentence.
Structural Knowledge – It is a basic problem-solving
knowledge that
describes the relationship between concepts and objects.

Procedural Knowledge – This is responsible for knowing
how to do
something and includes rules, strategies, procedures, etc.

Meta Knowledge – Meta Knowledge defines knowledge about
other
types of Knowledge.

Heuristic Knowledge – This represents some expert
knowledge in
the field or subject


6. Agents in Artificial
Intelligence
 In artificial intelligence, an intelligent agent is anything which
perceives its environment, takes actions autonomously in
order to
achieve goals, and may improve its performance with
learning or
may use knowledge.
 Agent
 An agent can be anything that perceive its environment
through
sensors .An Agent runs in the cycle of perceiving,
thinking,
and acting. An agent can be:

7. Agents in Artificial
Intelligence

8.  Human-
Agent:
A huma
n
agen
t
ha
s
eyes
,
ears
,
and othe
r
organs which
work
for actuators.
 Robotic Agent:
A
for sensors and hand, legs, vocal tract
work
robotic agent can have cameras,
infrared
range finder, NLP for sensors and various motors for
actuators.

Software
Agent
:
Softwar
e
agen
t
ca
n
hav
e
keystroke
s,
file
contents as sensory input and act on those inputs and
display
output on the screen.

9. Rules for an AI
agent
 Following are the main four rules for an AI agent:
 Rule 1: An AI agent must have the ability to
perceive the environment.

Rule

Rule

Rule
2:
3:
4:
The observation must be used to make decisions.
Decision should result in an action.
The action taken by an AI agent must be a rational
action.

10. AI
Agent

11. Structure
of
an AI
Agent
 The task of AI is to design an agent program which
implements the
agent function. The structure of an intelligent agent is a
combination
of architecture and agent program. It can be viewed as:
 Agent = Architecture + Agent program
 Following are the main three terms involved in the structure
of an AI
agent:
Architecture: Architecture is machinery that an AI agent
executes on.
Agent Function: Agent function is used to map a
percept to an action.
f:P* → A
Agent program: Agent program is an implementation of
agent
function. An agent program executes on the physical
architecture
to produce function f.
1.
2.
1.

12. Knowledge-
base

Knowledge
bas
e
is a centra
l
compone
nt
of a knowledge-
based
agent, it is also known as
KB.
 It is a collection of sentences (here 'sentence' is a technical
term and
it is not identica
l
to sentence
in
English
).
These
sentences
are
expressed in a language which is called a knowledge
representation
language. The Knowledge-base of KBA stores fact about the
world.
 Why use a knowledge base?
Knowledge-base is required for updating knowledge for an
agent
to learn with experiences and take action as per the
knowledge.

13. Knowledg
e
Based
System
A knowledge-based system (KBS) is a form of artificial
intelligence
(AI) tha
t
aim
s
to captur
e
th
e
knowledg
e
of huma
n
expert
s
to
suppo
rt
decision-
making.
Example
s
of knowledge-based
systems
include expert systems, which are so called because of their
reliance
on human expertise.

14. Knowledge-
based
agent
s
An intelligent agent needs knowledge about the real
world
for
taking decisions and reasoning to act
efficiently.
Knowledge-based agents are those agents who
have the
capability of
 maintaining an internal state of knowledge
 reason over that knowledge
 update their knowledge after observations and take
actions.
 These agents can represent the world with some
formal
representation and act intelligently.
 Knowledge-based agents are composed of two main
parts
 Knowledge-base and
 Inference system.

15. Knowledg
e
bas
e
Agen
t

16. Operations performed
by
KB
A
To show the intelligent behavior Knowledge base agent
perform
operations
followin
g
 TELL: This operation tells the knowledge base what it perceives
from the
environment.
 ASK: This operation asks the knowledge base what action it
should
perform.
 Perform: It performs the selected action.

17. Inference
system
Inference means deriving new sentences
from old.

an inference engine is a component of the system that
applies
logical rules to the knowledge base to deduce new
information.
Inference system allows us to add a new sentence to the
knowledge base. A sentence is a proposition about the world.
Inference system applies logical rules to the KB to deduce
new information.


Inference system generates new facts so that an agent can
update
the KB. An inference system works mainly in two rules
which are
given as:
Forward chaining
Backward chaining




18. Forward
Chaining
Forward chaining is a method of reasoning in artificial
intelligence
in which inference rules are applied to existing data to
extract
additional data until an endpoint (goal) is achieved.

19. Backward
Chaining
Backward chaining is a concept in artificial
intelligence
that involves backtracking from the endpoint or
goal to
steps that led to the endpoint

20. which
makes
Machine
valuable?

21. Knowledg
e
Representati
on
Knowledge
Representation
knowledge.
in AI describe
s
the representatio
n
of

Basicall
y,
it is a
study
of ho
w
the
ca
n
beliefs
,
intention
s,

and judgments of an intelligent
agent
for automated reasoning.
Example
be expressed
suitably

It is responsible for representing information about the real
world so
that a computer can understand and can utilize this
knowledge to
solve the complex real world problems such as diagnosis a
medical
condition or communicating with humans in natural language.

Knowledge representation in AI is not just about storing data
in a
database, it allows a machine to learn from that knowledge
and
behave intelligently like a human being.


22. Cycle
of
in AI
knowledge
representation
Artificial Intelligent Systems usually consist of
various
components to display their intelligent
behavior.

23. What is the Relation between
Knowledge &
Intelligence?
 In the real world, knowledge plays a
vital
as well as creating artificial
intelligence.
role in
intelligence
 It demonstrates the intelligent
behavior in
systems.
 It is possible for an agent or system
to act
AI agents
or
accurately on
some
input only when it has the knowledge or experience
about the
input.