A Lecture for GCET Students

September 11, 2009

Dr. Priti Srinivas SajjaDepartment of Computer Science

Sardar Patel UniversityVallabh Vidyanagar

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Introduction and Contact Information

• Name: Dr Priti Srinivas Sajja• Communication:

• Email : priti_sajja@yahoo.com• Mobile : 9824926020• URL: http://priti.sajja.info

• Academic Qualifications: Ph. D in Computer Science• Thesis Title: Knowledge-Based Systems for Socio-Economic

Rural Development• Subject area of specialization : Knowledge-Based Systems• Publications : 84 in International/ National Journals and Conferences

(Including two books and one chapter) • Academic Position : Associate Professor at

Department of Computer Science

Sardar Patel University

Vallabh Vidyanagar 388120

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Outlines of the Lecture

Part 1: Artificial Intelligence Natural intelligence and Artificial Intelligence Nature of AI Solutions Testing Intelligence Categories of Application Areas

Part 2: Symbolic Knowledge-Based Systems Data Pyramid and CBIS DBMS and KBS Structure of KBS Types of KBS Example KBS

Part 3: Connectionist Systems Symbolic and Connectionist Systems Example ANN for Course Selection

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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.

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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 towards problem solving in a symbolic and non-algorithmic way.

• AI is the branch of Computer Science that attempts to solve problems by mimicking human thought process using heuristics, symbolic and non-algorithmic approach in areas where people are better.

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where people are better

human thought process

characteristics we associate with intelligence

knowledge using symbols

heuristic methods

non-algorithmic

Figure 1.1: Constituents of artificial intelligence

Make Your Own Definition of AI

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Extreme solution, either best or worst taking (infinite) time

time

Acceptable solution in acceptable time

Figure 1.2: Nature of AI solutions

Nature of AI Solutions

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Testing Intelligence

Turing test will fail to test for intelligence in two circumstances;

1. A machine may well be intelligent without being able to chat exactly like a human; and;

2. 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

222222*67344?

Why Sir?

The Boss could not judge who was replying, thus the machine is as intelligent as the secretary.

Figure 1.4: The Turing test

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Application Areas of Artificial Intelligence

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 Formal Tasks

• Games - chess, backgammon, checkers, etc.

• Mathematics- geometry, logic, integral calculus, theorem proving, etc.

Expert Tasks• Engineering - design, fault

finding, manufacturing planning, etc.

• Scientific analysis • Medical diagnosis • Financial analysis

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Data Pyramid and Computer Based Systems

Data

Information

Knowledge

Wisdom

Understanding

Experience

Novelty

Researching Absorbing Doing Interacting Reflecting

Raw Data through fact finding

Concepts

Rules

Heuristics and models

Figure 1.6: Convergence from data to intelligence

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Data Pyramid and Computer-Based Systems

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

Figure 1.7: Data pyramid: Managerial perspectives

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Computer-Based Information Systems Tree

MIS

DSS

EES

ESS

ES

EIS

TPS

OAS

Figure 1.8: CBIS tree

1990

1970

1950

Hardware base/technology

Users’ requirements

IS

Intelligent systems: 21st century challenge

EES:

Executive expert system, which is a hybridization of an expert system , executive information system, and decision support system

Software resources

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Comparison of KBS with Traditional CBIS Systems

Traditional Computer-Based Information Systems (CBIS)

Knowledge-Based Systems (KBS)

Gives a guaranteed solution and

concentrate on efficiencyAdds 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 mannerWorks for narrow domains in a reactive and proactive manner

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Objectives of KBS

KBS is an example of fifth-generation computer technology. Some of its objectives are as follows:

• 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

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Components of KBS

Knowledge base

Inference engine

User interface

Explanation and

reasoning

Self-learning

Figure 1.10: General structure of KBS

Enriches the system with self-learning capabilities

Provides explanation

and reasoning facilitates

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

Friendly interface to

users working in their native language

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Categories of KBS

According to the classifications by Tuthhill & Levy (1991), five main types of KBS exists:

Expert systems

Linked systems CASE-based systems

Database in conjunction with an intelligent user interface

Intelligent tutoring systems

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Difficulties with the KBS

• Completeness of Knowledge Base

• Characteristics of Knowledge

• Large Size of Knowledge Base

• Acquisition of Knowledge

• Slow Learning and Execution

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An example of a Multi-agent KBS on Grid

Users

Experts

User In

terface Ag

ent

Agents

Learning Mgt.

Drills and Quizzes

Explanation

Semantic Search

E-mail & Chat

Resource Management

Question/Answer

Tutorial Path

Documentation

Inte

rne

t

Grid

Mid

dlew

are Services

Resource Management (Grid

Resource Allocation

Protocol-GRAM)

and

Grid FTP Replica-LocationServices

Information Discovery Services

Security Services

Distributed databases

Middleware Services and

Protocols

Local Data-Bases

Resources

Knowledge Mgt.

Meta knowledge

Conceptual system

Content knowledge

Learner’s ontology

Mail

Documents

Knowledge Discovery

Knowledge UtilizationKnowledge Management

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Communication Between Agents

• Agents developed here are communicating with a tool named KQML.

• Knowledge based Query Management Language.

(register

    : sender  agent_Lerning_Mgt

    : receiver agent_Tutorail-Path

    : reply-with   message

    : language     common_language

    : ontology     common_ontology

    : content      “content.data”

)

Action intended for the message

Agents name sharing message

Action intended for the message

Context-specific information describing the specifics of this message

Ontology of both the agents

Language of both agents

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Knowledge Representation of a Tutorial Topic: Array

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Prototype Screen Designs for the KBS

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Prototype Screen Designs for the KBS

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Result from the System

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An Example of a Connectionist System

Input Layer

Output Layer

Hidden Layers

Availability of expertise

Availability of hardware/based technology

Content /length of the course

Degree of assistance required[[[

Knowledge level required for the course/ depth of the course

Market trend towards technology/course

Personal interest

Success history if any (last few years result in%)

Time taken to complete (revision)

Bio-Informatics

suggested decision for Current Trends

Wireless Tech.

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Acknowledgement

Thanks to

GCET and Charutar Vidya Mandal

Reference

“Knowledge-Based Systems”

Rajendra Akerkar and Priti Srinivas Sajja

Book published by Jones and Bartlett Publishers, Massachusetts (MA), USA.