Knowledge Based Systems -Artificial Intelligence by Priti Srinivas Sajja S P University

Knowledge-Based Systems
Priti Srinivas Sajja
Associate Professor
Department of Computer Science
Sardar Patel University

Visit priti sajja.info for detail

Created By Priti Srinivas Sajja 1

Contact
Introduction
• Name: Dr. Priti Srinivas Sajja
Data Pyramid • Communication:
• Email : priti_sajja@yahoo.com
KBS • Mobile : +91 9824926020
Objectives and • URL :http://pritisajja.info
Characteristics
• Academic qualifications : Ph. D in Computer Science
Structure
• Thesis title: Knowledge-Based Systems for Socio-
Types of • Economic Development (2000)
Knowledge • Subject area of specialization : Artificial Intelligence
Knowledge
Acquisition • Publications : 106 in Books, Book Chapters, Journals and
Knowledge in Proceedings of International and National Conferences
Representation
Examples
2
Created By Priti Srinivas Sajja

Knowledge-Based SystemsThis slideshow is available here


Introduction
Introduction Natural Intelligence
• Responds to situations flexibly.
Data Pyramid • Makes sense of ambiguous or erroneous messages.
• Assigns relative importance to elements of a situation.
• Finds similarities even though the situations might be
KBS different.
Objectives and • Draws distinctions between situations even though there may
be many similarities between them.
Characteristics
Structure
Artificial Intelligence
Types of
• According to Rich & Knight (1991) “AI is the study of how to make
Knowledge computers do things, at which, at the moment, people are
Knowledge better”.
Acquisition • A machine is regarded as intelligent if it exhibits human
Knowledge characteristics generated through natural intelligence.
Representation • AI is the study of human thought processes and moving toward
problem solving in a symbolic and non-algorithmic way.
Examples
4

Introduction
Introduction

Data Pyramid

KBS
Objectives and
Characteristics
Structure “Artificial Intelligence(AI) is the study of how
Types of to make computers do things at which,
Knowledge at the moment, people are better”
Knowledge
Acquisition • Elaine Rich, Artificial Intelligence,
Knowledge McGraw Hill Publications, 1986
Representation
Examples
5

Introduction
Introduction
human thought process heuristic methods
Data Pyramid
where people are better non-algorithmic
KBS
characteristics we knowledge using
Objectives and associate with intelligence symbols
Characteristics
Constituents of artificial intelligence
Structure
Types of
Knowledge Acceptable solution Extreme solution, either best or
Knowledge in acceptable time worst taking  (infinite) time
Acquisition
Knowledge time
Representation
Nature of AI solutions
Examples
6

Introduction
Introduction Turing test will fail to test for
intelligence in two circumstances;
Data Pyramid 1. A machine may well be
intelligent without being
KBS Can you tell me
what is able to chat exactly like a
222222*67344?
human; and;
Objectives and
Why 2. The test fails to capture the
Characteristics Sir?
general properties of
Structure intelligence, such as the ability
to solve difficult problems or
Types of come up with original insights.
Knowledge If a machine can solve a
Knowledge difficult problem that no
Acquisition person could solve, it would,
Knowledge in principle, fail the test.
Representation
The Turing test
Examples
7

Introduction
Introduction Creating Your Own Test…
Data Pyramid
Can you find any test to check the given system is intelligent or not?
KBS
Reacts
Walks,
Objectives and differently
perceives, If it talks
Characteristics tests, smells, like
and feels like human
Makes and
human
Structure understands
joke
Types of
Knowledge Solves Translates,
Knowledge your summarizes,
problem and learns
Acquisition
Knowledge
Representation
Examples
8

Introduction
Introduction
Rich & Knight (1991) classified and described the different areas that
Data Pyramid Artificial Intelligence techniques have been applied to as follows:

KBS
Objectives and Mundane Tasks Expert Tasks
Characteristics • Perception - vision • Engineering - design,
and speech
Formal Tasks
fault finding,
• Natural language • Games - chess, manufacturing
Structure backgammon,
understanding, planning, etc.
generation, and checkers, etc.
Types of • Scientific analysis
translation • Mathematics-
Knowledge geometry, logic, • Medical diagnosis
• Commonsense
Knowledge integral calculus, • Financial analysis
reasoning
Acquisition theorem proving,
• Robot control etc.
Knowledge
Representation
Examples
9

Introduction

DataPyramid
Data Pyramid
IS

KBS
Strategy makers apply morals, principles, WBS Wisdom (experience)
and experience to generate policies
Objectives and
Characteristics Higher management generates KBS Knowledge (synthesis)
knowledge by synthesizing information
Structure Middle management uses reports/info.
DSS, MIS
generated though analysis and acts Information (analysis)
accordingly
Types of
Knowledge Basic transactions by operational TPS Data (processing of raw
staff using data processing observations )
Knowledge
Acquisition
Volume Sophistication and
Knowledge complexity
Representation
Examples
10

Introduction

DataPyramid
Data Pyramid
Heuristics
KBS and models Wisdom

Objectives and
Novelty
Characteristics Rules Knowledge

Structure
Information Experience
Concepts
Types of
Knowledge Data
Knowledge Raw Data through Understanding
fact finding
Acquisition Researching Absorbing Doing Interacting Reflecting
Knowledge
Representation
Examples
11

Introduction
Intelligent systems:
DataPyramid
Data Pyramid 21st century challenge
Software resources
IS
KBS
EES
Objectives and 1990
ES
Characteristics ESS
Users’ requirements EIS

Structure DSS
1970
OAS

Types of MIS
TPS
Knowledge 1950
Knowledge Hardware base/technology
Acquisition
Knowledge
Representation
Examples
12

Introduction

Data Pyramid

KBS
KBS K
Objectives and
Characteristics
Structure
Knowledge-Based Systems (KBS) are Productive
Types of
Knowledge Artificial Intelligence Tools working in a
Knowledge narrow domain.
Acquisition
Knowledge
Representation
Examples
13

Introduction Comparison
Traditional Computer-Based Information Knowledge-Based Systems (KBS)
Data Pyramid Systems (CBIS)
Gives a guaranteed solution and Adds powers to the solution and concentrates
concentrate on efficiency on effectiveness without any guarantee of
KBS
KBS solution
Data and/or information processing Knowledge and/or decision processing
Objectives and approach approach
Characteristics Assists in activities related to decision Transfer of expertise; takes a decision based
making and routine transactions; supports on knowledge, explains it, and upgrades it, if
Structure need for information required
Examples are TPS, MIS, DSS, etc. Examples are expert systems, CASE-based
Types of systems, etc.
Knowledge Manipulation method is numeric and Manipulation method is primarily
algorithmic symbolic/connectionist and nonalgorithmic
Knowledge
These systems do not make mistakes These systems learn by mistakes
Acquisition
Need complete information and/or data Partial and uncertain information, data, or
Knowledge knowledge will do
Representation Works for complex, integrated, and wide Works for narrow domains in a reactive and
areas in a reactive manner proactive manner
Examples
14

Introduction Categories of KBS
Data Pyramid
• Expert systems
KBS
KBS
• Linked systems
Objectives and • Intelligent tutoring system
Characteristics
• CASE based system
Structure • Intelligent user interface for databases
Types of
Knowledge
Knowledge
Acquisition
Knowledge
Representation
Examples
15

Introduction
• Provides a high intelligence level
Data Pyramid • Assists people in discovering and developing unknown
fields
KBS • Offers a vast amount of knowledge in different areas
Objectives and • Aids in management
Objectives
Characteristics • Solves social problems in better way than the traditional
CBIS
Structure
• Acquires new perceptions by simulating unknown
Types of situations
Knowledge • Offers significant software productivity improvement
Knowledge
Acquisition • Significantly reduces cost and time to develop
Knowledge computerized systems
Representation
Examples
16

Introduction Components of KBS
Data Pyramid
Knowledge base is a repository
of domain knowledge and meta Enriches the
knowledge. system with
KBS self-learning
Inference engine is a software
program, which infers the capabilities
Objectives and knowledge available in the
knowledge base
Characteristics
Structure
Structure Explanation
Knowledge
base
Inference
engine
and Self-
Types of reasoning
User interface
learning

Knowledge Friendly
Knowledge Provides interface to
explanation and users working
Acquisition reasoning in their native
facilitates language
Knowledge
Representation
Examples
17

Introduction Advantages and Difficulties
Data Pyramid • Permanent Documentation of Knowledge
• Cheaper Solution and Easy Availability of
KBS Knowledge
Objectives and • Dual Advantages of Effectiveness and Efficiency
Characteristics
Characteristics • Consistency and Reliability
Structure • Justification for Better Understanding
• Self-Learning and Ease of Updates
Types of
Knowledge • Completeness of Knowledge Base
Knowledge • Characteristics of Knowledge
Acquisition • Large Size of Knowledge Base
Knowledge • Acquisition of Knowledge
Representation • Slow Learning and Execution
• Development model and Standards
Examples
18

Introduction
Experience
Experts
Data Pyramid
Sources of Satellite
KBS Broadcasting
(Internet, TV,
Printed knowledge and Radio)
Objectives and Media
Characteristics
Types of Knowledge
Structure • Tacit knowledge
Types of • Explicit knowledge
Types of
Knowledge
Knowledge • Commonsense knowledge
Knowledge • Informed commonsense knowledge
Acquisition • Heuristic knowledge
Knowledge • Domain knowledge
Representation
• Meta knowledge
Examples
19

Introduction Knowledge Components
• Facts
Data Pyramid
– Facts represent sets of raw observation, alphabets, symbols, or
statements.
KBS • The earth moves around the sun.
• Every car has a battery.
Objectives and • Rules
Characteristics – Rules encompass conditions and actions, which are also known
as antecedents and consequences.
Structure • If there is daylight, then the Sun is in the sky.
• If the car does not start, then check the battery and fuel.
Types of
Types of • Heuristics
Knowledge
Knowledge – It is a rule of thumb, which is practically applicable however,
Knowledge does not offer guarantee of solution.
Acquisition • If there is total eclipse of the sun, there is no daylight, even
though the sun is in the sky.
Knowledge • If it is a rainy season and a car was driven through water,
Representation silencer would have water in it, so it may not start.

Examples
20

Introduction Inference Engine
Data Pyramid
An inference engine is a software program that refers the
existing knowledge, manipulates the knowledge according to
KBS
need, and makes decisions about actions to be taken.
Objectives and
Characteristics Match

Structure
Structure Conflict Setting
Knowledge Working
Types of Base Select Memory
Knowledge
Knowledge Execute

Acquisition
Knowledge Typical Inference Cycle
Representation
Examples
21

Introduction Forward Chaining
Data Pyramid
1. Consider initial facts and store them into working memory of the
knowledge base.
KBS
2. Check the antecedent part (left hand side) of the production rules.
Objectives and 3. If all the conditions are matched, fire the rule (execute the right
Characteristics hand side).
4. If there is only one rule do the following:
Structure
Structure
4.1 Perform necessary actions.
Types of 4.2 Modify working memory and update facts.
Knowledge
4.3 Check for new conditions.
Knowledge
5. If more than one rule is selected use the conflict resolution strategy
Acquisition
to select the most appropriate rules and go to step 4.
Knowledge
6. Continue until appropriate rule is found and executed.
Representation
Examples
22

Introduction Backward Chaining
Data Pyramid
1. Start with possible hypothesis, say H.
KBS 2. Store the hypothesis H in working memory along with the
available facts. Also consider a rule indicator R, and set it to
Objectives and Null.
Characteristics
3. If H is in the initial facts, the hypothesis it is proven. Go to
Structure
Structure step 7.
Types of 4. If H is not in the initial facts, find a rule, say R, that has a
Knowledge descendent (action) part mentioning the hypothesis.
Knowledge
5. Store R in working memory.
Acquisition
Knowledge 6. Check conditions of the R and match with the existing facts.
Representation
7. If matched, then fire the rule R and stop. Otherwise, continue
Examples to step 4.
23


A Short Break ….


IDENTIFICATION
Introduction Other CONCEPTULIZATION
Knowledge
Sources IDENTIFICATION
Knowledge Acquisition
Data Pyramid Experts Techniques Knowledge
KBS
requirements
• Literature review Engineer
• Protocol analysis
• Diagram-based techniques User
KBS • Concept sorting
Knowledge
representation
Knowledge • etc.
discovery and FORMALIZATION
Objectives and verification
IMPLEMENTATION

Characteristics Knowledge
Base
Data Base
Structure Automatic
creation from
TESTING
Cases and cases
documents
Types of
Knowledge
Knowledge
Knowledge Activities in the knowledge acquisition process
Acquisition
Acquisition • Find suitable experts and a knowledge engineer
Knowledge • Proper homework and planning
Representation • Interpreting and understanding the knowledge provided by the experts
• Representing the knowledge provided by the experts
Examples
25

Knowledge Acquisition
Introduction
• Problem Solving
Data Pyramid
• Talking and Story Telling
KBS
Objectives and • Supervisory Style
Characteristics
• Dealing with multiple experts
Structure
Types of
Knowledge
Knowledge
Knowledge Knowledge Group
Engineer Individual
Acquisition
Acquisition expert Hierarchical
handling
handling handling
Knowledge
Representation
Examples
26

Introduction Knowledge Update
Data Pyramid

KBS
Objectives and
Characteristics
Self-update by Update by expert
Structure system Update by knowledge
through interface
engineer
Types of
Knowledge
Knowledge
Knowledge
Acquisition
Acquisition
Knowledge
Representation
Examples
27

Knowledge Representation
Introduction
Constant: RAM, LAXMAN
Data Pyramid Variable: Man
Function: Elder (RAM, LAXMAN) returns any value, here, RAM
KBS Predicate: Mortal (RAM) returns a Boolean value, here, True
WFF: ‘If you do not exercise, you will gain weight is represented as:
Objectives and  x[{Human(x) ^ ~Exercise (x)}  Gain weight(x)]
Characteristics Factual Knowledge Representation
Structure
Types of Instance
Person
Instance
Knowledge
Knowledge Doctor
Agent
Give Patient
Acquisition Recipient

Knowledge
Knowledge Medicine
Representation
Representation
Frame
Examples
28

Knowledge Representation
Introduction
Name: Visit to Pharmacy Scene 1: Entry
P enters to the pharmacy.
Data Pyramid Props: Money P goes to reception. P meets R.
Symptoms P pays registration and/or fees and gets appointment.
Treatment Go to Scene 2.
Medicine
KBS
Roles: Dentist - D
Scene 2: Consulting Doctor
Objectives and Receptionist - R
Patient - P
P meets D.
P conveys symptoms.
Characteristics
Entry Conditions:
P gets treatment. P gets appointment.
Structure Patient P has toothache.
Patient P has money.
Go to Scene 3.
Types of Exit Conditions
Knowledge Patient P has less money.
Patient P returns with treatment.
Scene 3: Exiting
P pays money to R.
Knowledge Patient P has appointment. P exits the pharmacy.
Patient P has prescription.
Acquisition
Knowledge
Knowledge
Representation
Representation
Examples
29

Examples

Typology


Examples


Examples
Introduction

Data Pyramid
• ELIZA is a computer program and an early example of
KBS primitive natural language processing.

Objectives and • ELIZA was written at MIT by Joseph Weizenbaum
Characteristics between 1964 to 1966.
Structure • ELIZA was implemented using simple pattern matching
techniques, but was taken seriously by several of its
Types of
Knowledge
users, even after Weizenbaum explained to them how
Knowledge it worked.
Acquisition • It was one of the first chatterbots in existence.
Knowledge
Representation
Examples
Examples
42

Examples
// Description: this is a very basic example of a chatterbot program by Gonzales Cenelia
#include <iostream>
#include <string>
#include <ctime>
int main() {
std::string Response[] = {"I HEARD YOU!", "SO, YOU ARE TALKING TO ME.",
CONTINUE, I AM LISTENING.", "VERY INTERESTING CONVERSATION.", "TELL ME
MORE..." };
srand((unsigned) time(NULL));
std::string sInput = "";
std::string sResponse = "";
while(1)
{ std::cout << ">";
std::getline(std::cin, sInput);
int nSelection = rand() % 5;
sResponse = Response[nSelection];
std::cout << sResponse << std::endl;
}
return 0;
} Created By Priti Srinivas Sajja 43

Introduction

Data Pyramid

KBS
Objectives and
Characteristics
Structure
Types of
Knowledge
Knowledge
Acquisition
Knowledge
Representation
Examples
44

Knowledge Based Systems -Artificial Intelligence by Priti Srinivas Sajja S P University

Knowledge Based Systems -Artificial Intelligence by Priti Srinivas Sajja S P University

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Knowledge Based Systems -Artificial Intelligence by Priti Srinivas Sajja S P University