The document provides an overview of artificial intelligence and knowledge-based systems. It discusses definitions of intelligence and AI, as well as knowledge representation schemes like logical, procedural, semantic network, and frame-based representations. The key components of a knowledge-based system are described as the knowledge base, which represents problem domain knowledge, and the inference engine, which uses reasoning techniques to solve problems. Ideal features of knowledge-based systems include efficient problem-solving using knowledge, heuristics, and eliminating unproductive solutions.

1. Fundamentals of
ARTIFICIAL INTELLIGENCE
Rajendra Akerkar

2. 2
INTRODUCTION
• What is “intelligence”?
intelligence ?
▫ no single exact definition
▫ what seems intelligent to one person, may
person
not be so, for another person

3. 3
• Intelligence is studied from many
perspectives
▫ hardcore AI: computer scientists
creating theories and programs to solve
computationally difficult problems
▫ psychology: psychologists interested in
h l h l i i di
human intelligence
▫ cognitive scientists: similar to AI and
psych schools, except they want to
implement human models of intelligence
on the computer (ie. simulate neurology
behind vision)

4. 4
• Following characteristics are
g
suggestive of essential abilities for
possessing intelligence
▫ responding to situations, flexibly
▫ making sense of ambiguous/noisy
messages
▫ assigning relative importance to
elements of a situation
▫ finding similarities in situations even
though the situations might be different
▫ddrawing distinctions between situations
i di i i b i i
even though there may be many
similarities between them

5. 5
• Assuming that the mentioned
g
characteristics suggest the possession
of intelligence, following are examples
of tasks that require i
f k h i intelligence
lli
▫ speech generation and understanding
h ti d d t di
▫ painting a sensible picture
▫ recognizing the face of a friend
▫ understanding a story or a fairy tale
▫ understanding a moral delivered in a
g
discourse
▫ making decisions, e.g. a doctor or a
company didirector
t

6. 6
▫ finding the shortest tour to visit a
number of places
▫ playing chess well
▫ moving in a dynamic obstacle filled
space
▫ mathematical theorem proving
h i l h i
▫ giving explanations
▫ writing a program etc.
program, etc
With this overview, some of the
definitions of “Artificial Intelligence”
are as follows

7. 7
• Artificial Intelligence (AI), is the study
of how to make computers do things, at
which, at the moment, humans are
better.
• Artificial Intelligence (AI) is the branch
of computer science dealing with
f i d li ih
symbolic methods of problem solving.
• Artificial Intelligence (AI) is the study
of how to make computers get
knowledge from information, store,
update, and use it for problem-solving
in an environment, so as to reach the
desired goal.

8. 8
But why computers?
y p
• Numerical computations
▫ computers are definitely faster and
more accurate
• Information storage
▫ computers can store very h
t t huge amounts
t
of information
• Repetitive operations
▫ computers don’t get fatigued or bored

9. 9
How does the computer become
artificially intelligent?
• The program running on the computer
makes it seem intelligent
• in fact it is this program which is
artificially intelligent
• such programs are called artificial
intelligence(ai) programs
g ( )p g

10. 10
AI Programs
g
• A complete AI program consists of two
components, namely,
▫ knowledge base, and,
▫ inference/reasoning engine
• AI programs can be written in high level
languages like, C, C++, etc., or in special
purpose artificial intelligence languages
like, Lisp, Prolog, etc.

11. 11
• The knowledge base represents the
knowledge of the problem domain.
Several knowledge representation
g p
models exist.
• The inference/reasoning engine is an
algorithm which embodies the
capability to “search” for a solution in
the i
th given knowledge base, for the
k l d b f th
relevant situation.
• In principle the AI languages provide
principle,
in-built search capabilities.

12. INFERENCE ENGINE

13. 13
Definition
• An algorithm that
▫ concludes by LOGICAL DEDUCTION using
the Knowledge Base
▫ SEARCHES for conclusion in the
S C S
Knowledge Base
▫ GENERATES the conclusion by a mixed
method of LOGICAL DEDUCTION and
h d f d
SEARCH techniques

14. 14
Logical Deduction
g
Example
Assume that we have the following facts
F(1): If it is hot and humid, then it will rain
F(2): If it is humid then it is hot
humid,
F(3): It is humid now
The question is: Will it rain?

15. 15
The given facts are in English
gi en
We shall use symbols to represent them.
Let
P <=> It is hot
Q <=> It is humid
R < > It will rain
<=>
^ <=> and
-> <=> imply
py

16. 16
Using the symbols mentioned, the facts
i h b l i d h f
stated can be represented as follows
F(1) : P ^ Q -> R
F(2) : Q -> P
F(3) : Q
In the above form of representation the
representation,
facts are now called as logical
formulas, hence the deduction is
,
operating on “symbolic logic”

17. 17
Conclusion
F(2) follows F(3)
F(3) says it is humid, F(2) says, since it is
humid says
humid, it is hot.
F(1) follows F(2)
F(2).
Since F(2) says it is hot, and F(3) says it is
humid,
humid hence F(1) says “it will rain”.
it rain

18. 18
Logic
g
LOGIC is the ART OF “CORRECT”
REASONING/INFERENCING
but
What is meant by “CORRECT”?
CORRECT ?

19. 19
CORRECTNESS
For the reasoning process to be called
“CORRECT”, it should possess the
CORRECT
following two properties
COMPLETENESS
SOUNDNESS

20. 20
COMPLETENESS
This is the property of a reasoning process
p p y gp
to conclude “ALL” the true facts over the
given set of statements

21. 21
SOUNDNESS
This the property of the reasoning process,
to conclude no “WRONG” fact over the
given set of statements

22. 22
Prepositional Logic
• Simplest form of symbolic logic
• Here we are interested in declarative
statements that can be either TRUE or
FALSE, but not both!
Definition
A““preposition” i a declarative
iti ” is d l ti
statement which is either TRUE or
FALSE but not both.

23. 23
Logical Consequences
g q
Definition
Given formulas F1, F2, … , Fn and a
F1 F2
formula G, G is said to be a logical
consequence of F1, F2, … , Fn (or G
logically follows from F1, F2, … , Fn) if
and only if, for any interpretation I in
which F1 ^ F2 ^ … ^ Fn is TRUE, G is also
TRUE

24. 24
Theorem 1
Given formulas F1, F2, … , Fn , and a
formula G G is said to a “logical
G, logical
consequence” of F1, F2, … , Fn, if and only
if, the formula
((F1 ^ F2 ^ … ^ Fn) -> G)
is valid

25. 25
Theorem 2
Given the formulas F1, F2, … , Fn and a
formula G G is said to be a “logical
G, logical
consequence” of F1, F2, … , Fn, if and only
if, the formula
(F1 ^ F2 ^ … ^ Fn ^ ~G)
is inconsistent

26. KNOWLEDGE BASE

27. 27
Knowledge Representation Schemes
• Logical representation
• Procedural representation
• Network representation
• Structured Representation schemes

28. 28
Logical Representation Schemes
g p
• Representation in formal Logic
▫ Prepositional
▫ Predicate
• Rules can be considered as a subset of Predicate
logic
• Prolog is an ideal language for implementing
g g g p g
this.

29. 29
Procedural Representation Scheme
• Represents Knowledge as a set of instructions
for solving a problem
• Rule based system is an example of this

30. 30
Network Representation Schemes
• Semantic Network
▫ Maps of relationships utilizing nodes and links
• Conceptual Graphs
▫ Nodes in the maps are concepts or conceptual
relations.
l ti
Associationist theories define the meaning of an
object in the terms of a network of associations with
other objects in the mind or a KB.
Graphs by providing a means of explicitly
representing relations using arcs and nodes, h
i l i i d d have
proved to be an ideal vehicle for formalizing
associationist theories of knowledge.

31. 31
Some Principles of Semantic
Networks
• Semantic nets describe relationship
between things that are represented as
nodes
• The nodes are circles that have names
• The relationship between nodes re
h l i hi b d
represented by arcs that connect the circles.
• A semantic net can be used to generate
se a t c et ca ge e ate
▫ structures and objects.
▫ Rules for a knowledge base
Thus a semantic network represents
knowledge as a graph with the nodes
corresponding to facts or concepts, and arcs
to relations or associations between
concepts.

32. 32
Conceptual Graphs
A conceptual graph is a finite, connected, bipartite
graph.
Features
• Concept nodes represents either concrete or
abstract objects in the world of discourse
discourse.
• Conceptual relation nodes indicate a relation
involving one or more concepts
• Each conceptual graph represents one single
h l h i l
proposition. A typical KB may contain a number of
such graphs. Graph may be arbitrarily complex, but
must b finite
be fi i
• Theory of Conceptual graphs includes a number of
operations that allow us to form new graphs from
p g p
existing graphs

33. 33
Structured Representation S h
S dR i Schemes -
FRAMES
• Extends semantic net in a number of
important ways
• Procedural attachment is an important
feature of frames.
• Representing knowledge with frame
system allows us to reason at least to some
extent, even though the information is
incomplete, and quickly infer facts that
p , q y
are not explicitly observed.
• One problem with frames is the difficulty
for establishing default value for a frame
accurately.

34. 34
Structured Representation
Schemes - SCRIPTS
A representation describing stereo type sequence of
p g yp q
events in particular context.
Components
• Entry conditions - Description of the world that
must be true for the script to be called
• Results - Fact that are true when the script is
terminated.
terminated
• Props - Things that make up the context of the
script.
• R l - A ti
Roles Actions of the individual participant that
f th i di id l ti i t th t
form the actions of the scripts.
• Scenes - Subparts of the script, Formed by breaking
the script into parts on temporal aspect.
h i i l

35. 35
Technique for dealing with
complexity
• Certainty
▫ A mathematical property that attaches a
confidence factor to the conclusion reached by
rules
• Modularization
▫ Partitioning the rule base into modules
• Blackboard
l kb d
▫ Concept is similar to a group of experts working
out the problem by standing around a black board

36. 36
Technique for Dealing with
Complexity
• Blackboard
▫ Control Blackboard
Means of controlling the flow of a KB system by allowing the
module to schedule and prioritize p
p processing
g
▫ Data Blackboard
Means of processing information from one module of a system
to another
• External Data Sources
▫ Making use of sensors, historical data, data bases, etc. to avoid
asking the users
• Back tracking
▫ The retreat of the IE from the examination of the current
hypothesis in order to pursue another.

37. 37
Knowledge Based Systems
g y
- Desired Features
Ideal KB System should
• Construct solutions selectively and efficiently from a space of
alternatives.
• Identify useful ones and explore them further.
• Keep eliminating not so useful ones till an optimal solution is
obtained
Intelligent Problem solving activity
• Uses knowledge about that domain
Knowledge = beliefs+facts+heuristics
• To achieve necessary success
Success = finding a good solution with the available
g g
resources.

38. 38
Intelligent Problem Solving
Activity
Factor responsible for efficient solutions
• Applicable, correct and discriminatory knowledge
• Elimination of unproductive views
• Multiple cooperative sources of knowledge
• Dividing the solution at various levels of
abstraction
Factor which lead to difficulties
• Wrong and errorful knowledge
• Number of possibilities mighty be large
• Complex procedures to rule them out
• Dynamically changing problem

39. 39
Architecture of a Knowledge Based System
g y
Language
Processor Facts and Rules
Justifier
Plan Interpreter
p
Agenda Scheduler
S h d l
Consistency
Solution
Enforcer

40. 40
Ideal Architecture of a Knowledge Based
g
System
Language Interface
g g
To help the user to communicate in a problem oriented
way, handles user questions, commands
Provide justifications, and request for data when needed.
justifications needed
Plan
A General method to attack problems in the domain
Agenda
Various actions that are applicable at any stage of the
p
problem solving g
Solution
Record the partial solution of the problem.

41. 41
Ideal Architecture of an Knowledge Based
g
System
Scheduler
Maintains control of the agenda and determines which pending
action has to be executed next.
Interpreter
I t t
Executes a chosen agenda item by applying the corresponding
KB rule. Validates the relevant conditions.
Consistency Enforcer
It tries to maintain consistent representation of the emerging
solution
Justifier
Provides Explanation facility, answering user questions regarding
system actions
t ti

42. Knowledge Based Systems vs
Conventional Programs
Conventional KB Systems
Data Processing Knowledge Processing
Representation and use of Representation and use of
static data data+control=knowledge
Algorithms Heuristics
Repetitive Process Inferential Process
Few control and Large data Large control and few data
data,
kept seperately kept together
42

43. 43
Generic Knowledge Based System
Architecture
Inference Engine
User
Interface
Knowledge Base

44. 44
Generic Knowledge Based System
g y
Architecture
User Interface (UI)
•Editor to Input Knowledge
•Knowledge debugger
K l d d b
•Display conclusion
•Request for data
User
Interface
•Explanation of actions
Knowledge Base

45. 45
Generic Knowledge Based System
Architecture
Knowledge Base
• Represents the knowledge of the problem
domain.
domain
• Several knowledge representation models exist.
Inference/Reasoning Engine
•Algorithm which embodies the capability to
“search” for a solution in the given knowledge
base, for the relevant situation.
• AI l
languages provide i b ilt search
id in-built h
capabilities.

46. 46
Knowledge Based System
Development Phases
Identifying Problem
y g
Characteristics Requirements
IDENTIFICATION
Find concepts to
Concepts
C
Represent K.B.
CONCEPTUALIZATION
Reformulation
Design structures to
Structures
organize knowledge
FORMALIZATION
Reformulation
Formulate rules to
Redesign
embody knowledge Rules
IMPLEMENTATION
Validate rules
TESTING
Acquisition and Organisation Representation and Implementation

47. 47
Knowledge Based System
g y
Development Phases
• Identification
– Participants
– Problem
• Class of problems ES expected to solve
• Definition and characterization
• Sub
S b problems and partitioning of the t k
bl d titi i f th tasks
• Data available
• Important terms and interrelations
p
• Required kind of solutions
• Aspect of human expertise essential
– Resource
– Goal

48. 48
Knowledge Based System
Development Phases
• Conceptualization
p
– Make concepts and relationship identified in the
earlier stages more explicit
• What type of data available ?
• What is given and what has to be inferred ?
• Do sub tasks have names ?
• Do strategies have names ?
• Are there identifiable partial hypothesis that are
commonly used ? If so what are they ?
• Can we represent concepts and relationships
d g
diagrammatically ?
c y
• What are the constrain on these processes ?
• What is the information flow pattern ?

49. 49
Knowledge Based System
Development Phases
• Formalisation
– Involves mapping the key concepts, subproblems, and
information flow characteristics identified in the
previous stage into more formal representation based
on various knowledge engineering tools.
– Knowledge Engineer has to identify the suitable shell.
• Knowledge Representation Format
• Data types provided
• Inferencing strategy

50. 50
Knowledge Based System
g y
Development Phases
• Formalisation
– Concepts are structured objects or primitives ?
– Is casual or spatio-temporal relationships among concepts inportant ?
– Are the concept and hypothesis space finite or not?
– Are there uncertainties and other judgemental elements related to the final
and intermediate hypothesis ?
– Is hypothesis hierarchy present or not?
– Type of process model purely judgemental or mathmatical and
judgemental ?
– D t model d
Data d l depends on
d
• Completeness, consistency
• Is there any relationship between logical interpretation and their order
of occurrence over time ?

51. 51
Knowledge Based System
g y
Development Phases
• Implementation
– Mapping the formalized knowledge from the
previous stage into the representational frame
i i i f
work.
– Development of a prototype system is extremely
important

52. 52
Knowledge Based System
Development Phases
• Testing
– Evaluating the prototype and representational
forms.
– Test the prototype with examples
– Test with real world problems.
–CCauses of poor performance
f f
• I/O characteristics which refers to knowledge acquisition and
conclusion presentation
• Incorrect, incomplete, and inconsistent inference rules
• Control strategy (sequencing the rules)
• Test example selection (Homogeneous examples)

53. 53
Intelligent Agents
• What is an Agent ?
• What are a multi agent systems ?
• How i i
H it is used for solving problems ?
df l i bl
• Stages involved in the development
process.

54. 54
What is an Agent ?
g
A simple way to conceptualize an agent is that of a
process (software) which has some properties
listed below.
• Autonomy
▫ Ability to operate without direct intervention of
humans or others.
• Social Ability
▫ Ability to communicate with human and other agents
• Pro-activeness
Pro activeness
▫ Ability to take initiative and exhibit goal directed
behaviour.
• Reactivity
▫ Ability to perceive the environment respond to it’s
changes
• Intelligence
▫ Have human like mentalistic notions of knowledge,
beliefs, intentions and obligations

55. 55
What is an Agent ?
• Veracity
▫ Not knowingly communicating false information.
• Benevolence
▫ Assumption that agents do not have conflicting goals
• Rationality
▫ Acting to achieve its goals and not preventing their
achievement.
achievement
• Selectivity
▫ Ability to focus attention on what is needed and ignoring
the rest
• Robustness
▫ Ability to cope up with failures and tolerate
imperfections
A close look at an Agent reveal that basically it is an
Knowledge Based System with inherent processing
g y p g
powers besides
deduction.

56. 56
Multi Agent Systems
g y
• Systems Comprising of multiple
autonomous agents
agents.
ISSUES
• Homogeneity of the Knowledge
representation
p
• Agent Communication Protocol
• Topology
• Reliability and Security of Communication

57. 57
System Status Monitor
• Consider a Production Plant
id d i l
• It may have many complex sub systems
• St t of th plant will d
Status f the l t ill depend ond
status of all the subsystems
• Each subsystem can have various states
• Based on the state of each sub system,
certain action has to be taken for
smooth functioning of the Plant

58. 58
System Status Monitor
- An Agent based Perception
System Monitor
Agent
Agent -1 Agent -2 Agent -n
Sub system Sub system Sub system
1 1 1

59. 59
Multi Agent Systems
- Hierarchical
Agent - 0
Agent -1/1 Agent -2/1
Agent -1/2 Agent -2/2 Agent -3/2 Agent -4/2
... ... ...

60. 60
Agent Oriented Analysis & Design
• Extension of Object Oriented Analysis & Design
• Only Agents can perceive events, perform actions.
Objects are passive entities with no such capacities.
• State of an Object has no generic structure but an
Agent has mentalistic structure consists of mental
A h li i i f l
component such as beliefs .
• Messages in OO Systems are coded in application
specific manner but Agent Communication
Language can be application independent.

61. 61
Agent O e ed Analysis &
ge Oriented ys s
Design
• Abstraction level of Object Oriented Analysis & Design
should be level at which each object represents an Agent
(Knowledge Based System).
• Based on the structure, each agent can be developed
individually
i di id ll as explained in the Knowledge Based
l i di h l d d
Systems development process.
• All the required abilities should be implemented as the
th i d biliti h ld b i l t d th
part of the Knowledge Based System to make it as an
Agent.
Agent