1. Knowledge-Based Systems
Priti Srinivas Sajja
Associate Professor
Department of Computer Science
Sardar Patel University
Visit priti sajja.info for detail H
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Contact
Name: Dr. Priti Srinivas Sajja
Communication:
Mobile :
URL :
Academic qualifications : Ph. D in Computer Science
Thesis title: Knowledge-Based Systems for Socio-
Economic Development
Subject area of specialization : Artificial Intelligence
Publications : 84 in Books, Book Chapters, Journals and in Proceedings of International and National Conferences
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Introduction
Natural Intelligence
Responds to situations flexibly.
Makes sense of ambiguous or erroneous messages.
Assigns relative importance to elements of a situation.
Finds similarities even though the situations might be different.
Draws distinctions between situations even though there may be many similarities between them.
Artificial Intelligence
According to Rich & Knight (1991) “AI is the study of how to make computers do things, at which, at the moment, people are better”.
A machine is regarded as intelligent if it exhibits human characteristics generated through natural intelligence.
AI is the study of human thought processes and moving toward problem solving in a symbolic and non-algorithmic way.
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Introduction
“Artificial Intelligence(AI) is the study of how to make computers do things at which, at the moment, people are better”
Elaine Rich, Artificial Intelligence,
McGraw Hill Publications, 1986
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6. Constituents of artificial intelligence
Introduction
where people are better
human thought process
characteristics we associate with intelligence
knowledge using symbols
heuristic methods
non-algorithmic AI
Constituents of artificial intelligence
Extreme solution, either best or worst taking  (infinite) time
time
Acceptable solution in acceptable time
Nature of AI solutions
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Introduction
Turing test will fail to test for intelligence in two circumstances;
A machine may well be intelligent without being able to chat exactly like a human; and;
The test fails to capture the general properties of intelligence, such as the ability to solve difficult problems or come up with original insights. If a machine can solve a difficult problem that no person could solve, it would, in principle, fail the test.
Can you tell me what is *67344?
Why Sir?
The Boss could not judge who was replying, thus the machine is as intelligent as the secretary.
The Turing test
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8. Creating Your Own Test…
Introduction
Creating Your Own Test…
Can you find any test to check the given system is intelligent or not?
Reacts differently
Walks, perceives, tests, smells, and feels like human
If it talks like human
Makes and understands joke
Solves your problem
Translates, summarizes, and learns
conceptually form a test
and use it in different situation
before accepting it.
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Introduction
Rich & Knight (1991) classified and described the different areas that Artificial Intelligence techniques have been applied to as follows:
Mundane Tasks
Perception - vision and speech
Natural language understanding, generation, and translation
Commonsense reasoning
Robot control
Expert Tasks
Engineering - design, fault finding, manufacturing planning, etc.
Scientific analysis
Medical diagnosis
Financial analysis
Formal Tasks
Games - chess, backgammon, checkers, etc.
Mathematics- geometry, logic, integral calculus, theorem proving, etc.
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10. Sophistication and complexity
Data Pyramid
Basic transactions by operational staff using data processing
Middle management uses reports/info. generated though analysis and acts accordingly
Higher management generates knowledge by synthesizing information
Strategy makers apply morals, principles, and experience to generate policies
Wisdom (experience)
Knowledge (synthesis)
Information (analysis)
Data (processing of raw observations )
Volume
Sophistication and complexity
TPS
DSS, MIS
KBS
WBS IS
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11. Raw Data through fact finding
Data Pyramid
Data
Information
Knowledge
Wisdom
Understanding
Experience
Novelty
Researching Absorbing Doing Interacting Reflecting
Raw Data through fact finding
Concepts
Rules
Heuristics and models
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12. Hardware base/technology
Data Pyramid
MIS
DSS
EES
ESS ES
EIS
TPS
OAS
1990
1970
1950
Hardware base/technology
Users’ requirements IS
Intelligent systems: 21st century challenge
EES:
Executive Expert System is a hybridization of an expert system , executive information system, and decision support system.
Software resources
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13. Knowledge-Based Systems Created By Priti Srinivas Sajja
KBS
Knowledge-Based Systems (KBS) are Productive Artificial Intelligence Tools working in a narrow domain.
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Comparison
Traditional Computer-Based Information Systems (CBIS)
Knowledge-Based Systems (KBS)
Gives a guaranteed solution and concentrate on efficiency
Adds powers to the solution and concentrates on effectiveness without any guarantee of solution
Data and/or information processing approach
Knowledge and/or decision processing approach
Assists in activities related to decision making and routine transactions; supports need for information
Transfer of expertise; takes a decision based on knowledge, explains it, and upgrades it, if required
Examples are TPS, MIS, DSS, etc.
Examples are expert systems, CASE-based systems, etc.
Manipulation method is numeric and algorithmic
Manipulation method is primarily symbolic/connectionist and nonalgorithmic
These systems do not make mistakes
These systems learn by mistakes
Need complete information and/or data
Partial and uncertain information, data, or knowledge will do
Works for complex, integrated, and wide areas in a reactive manner
Works for narrow domains in a reactive and proactive manner
KBS
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Categories of KBS
Expert systems
Linked systems
Intelligent tutoring system
CASE based system
Intelligent user interface for databases
KBS
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Provides a high intelligence level
Assists people in discovering and developing unknown fields
Offers a vast amount of knowledge in different areas
Aids in management
Solves social problems in better way than the traditional CBIS
Acquires new perceptions by simulating unknown situations
Offers significant software productivity improvement
Significantly reduces cost and time to develop computerized systems
Objectives
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17. Components of KBS Structure Created By Priti Srinivas Sajja
Knowledge base is a repository of domain knowledge and meta knowledge.
Inference engine is a software program, which infers the knowledge available in the knowledge base
Enriches the system with self-learning capabilities
Structure
Knowledge base
Inference engine
User interface
Explanation
and
reasoning
Self-
learning
Friendly interface to users working in their native language
Provides explanation and reasoning facilitates
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18. Advantages and Difficulties Created By Priti Srinivas Sajja
Permanent Documentation of Knowledge
Cheaper Solution and Easy Availability of Knowledge
Dual Advantages of Effectiveness and Efficiency
Consistency and Reliability
Justification for Better Understanding
Self-Learning and Ease of Updates
Characteristics
Completeness of Knowledge Base
Characteristics of Knowledge
Large Size of Knowledge Base
Acquisition of Knowledge
Slow Learning and Execution
Development model and Standards
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19. Satellite Broadcasting (Internet, TV, and Radio)
Experience
Satellite Broadcasting (Internet, TV, and Radio)
Printed Media
Experts
Sources of knowledge
Types of Knowledge
Tacit knowledge
Explicit knowledge
Commonsense knowledge
Informed commonsense knowledge
Heuristic knowledge
Domain knowledge
Meta knowledge
Types of Knowledge
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Knowledge Components
Facts
Facts represent sets of raw observation, alphabets, symbols, or statements.
The earth moves around the sun.
Every car has a battery.
Rules
Rules encompass conditions and actions, which are also known as antecedents and consequences.
If there is daylight, then the Sun is in the sky.
If the car does not start, then check the battery and fuel.
Heuristics
It is a rule of thumb, which is practically applicable however, does not offer guarantee of solution.
If there is total eclipse of the sun, there is no daylight, even though the sun is in the sky.
If it is a rainy season and a car was driven through water, silencer would have water in it, so it may not start.
Types of Knowledge
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21. Typical Inference Cycle
Inference Engine
An inference engine is a software program that refers the existing knowledge, manipulates the knowledge according to need, and makes decisions about actions to be taken.
Match
Select
Execute
Conflict Setting
Knowledge Base
Working Memory
Typical Inference Cycle
Structure
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Forward Chaining
Consider initial facts and store them into working memory of the knowledge base.
Check the antecedent part (left hand side) of the production rules.
If all the conditions are matched, fire the rule (execute the right hand side).
If there is only one rule do the following:
Perform necessary actions.
Modify working memory and update facts.
Check for new conditions.
If more than one rule is selected use the conflict resolution strategy to select the most appropriate rules and go to step 4.
Continue until appropriate rule is found and executed.
Structure
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Backward Chaining
Start with possible hypothesis, say H.
Store the hypothesis H in working memory along with the available facts. Also consider a rule indicator R, and set it to Null.
If H is in the initial facts, the hypothesis it is proven. Go to step 7.
If H is not in the initial facts, find a rule, say R, that has a descendent (action) part mentioning the hypothesis.
Store R in working memory.
Check conditions of the R and match with the existing facts.
If matched, then fire the rule R and stop. Otherwise, continue to step 4.
Structure
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A Short Break ….
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25. Other Knowledge Sources
IDENTIFICATION
Experts
Other Knowledge Sources
CONCEPTULIZATION
IDENTIFICATION
Knowledge Acquisition Techniques
Literature review
Protocol analysis
Diagram-based techniques
Concept sorting
etc.
KBS requirements
Knowledge Engineer
User
Knowledge representation
Knowledge discovery and verification
IMPLEMENTATION
FORMALIZATION
Knowledge Base
Data Base
Cases and documents
Automatic creation from cases
TESTING
Activities in the knowledge acquisition process
Knowledge Acquisition
Find suitable experts and a knowledge engineer
Proper homework and planning
Interpreting and understanding the knowledge provided by the experts
Representing the knowledge provided by the experts
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26. Knowledge Acquisition
Problem Solving
Talking and Story Telling
Supervisory Style
Dealing with multiple experts
Knowledge Engineer
Hierarchical handling
Group handling
Individual expert handling
Knowledge Acquisition
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27. Update by knowledge engineer Update by expert through interface
Knowledge Update
Update by knowledge engineer
Self-update by system
Update by expert through interface
Knowledge Acquisition
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28. Knowledge Representation
Constant: RAM, LAXMAN
Variable: Man
Function: Elder (RAM, LAXMAN) returns any value, here, RAM
Predicate: Mortal (RAM) returns a Boolean value, here, True
WFF: ‘If you do not exercise, you will gain weight is represented as:
 x[{Human(x) ^ ~Exercise (x)}  Gain weight(x)]
Factual Knowledge Representation
Name: Power Bike
Broad Category: Land Vehicle
Sub Category: Gearless
Fuel Type: Gas
Cost: $ 350
Capacity: Two persons
Speed: Km/Hour
Person
Doctor
Patient
Medicine
Give
Instance
Agent
Recipient
Semantic Network
Knowledge Representation
Frame
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29. Knowledge Representation Created By Priti Srinivas Sajja
Name: Visit to Pharmacy
Props: Money
Symptoms
Treatment
Medicine
Roles: Dentist - D
Receptionist - R
Patient - P
Entry Conditions:
Patient P has toothache.
Patient P has money.
Exit Conditions
Patient P has less money.
Patient P returns with treatment.
Patient P has appointment.
Patient P has prescription.
Scene 1: Entry
P enters to the pharmacy.
P goes to reception. P meets R.
P pays registration and/or fees and gets appointment.
Go to Scene 2.
Scene 2: Consulting Doctor
P meets D.
P conveys symptoms.
P gets treatment. P gets appointment.
Go to Scene 3.
Scene 3: Exiting
P pays money to R.
P exits the pharmacy.
Knowledge Representation
Script
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Examples
Typology
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Examples
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Examples
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Examples
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Examples
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Examples
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Examples
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Examples
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Examples
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Examples
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Examples
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Examples
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Examples
ELIZA is a computer program and an early example of primitive natural language processing.
ELIZA was written at MIT by Joseph Weizenbaum between 1964 to 1966.
ELIZA was implemented using simple pattern matching techniques, but was taken seriously by several of its users, even after Weizenbaum explained to them how it worked.
It was one of the first chatterbots in existence.
Examples
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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;
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