MANAGEMENT INFORMATION SYSTEM

Third Year Information Technology

Part 07Expert Systems

Tushar B Kute,Department of Information Technology,Sandip Institute of Technology and Research Centre, Nashikhttp://www.tusharkute.com

EXPERT SYSTEM ARCHITECTURE (1)

The typical architecture of an e.s. is often described as follows:

useruser

interface

inference

engine

knowledge

base

EXPERT SYSTEM ARCHITECTURE (1)

The inference engine and knowledge base are separated because: the reasoning mechanism needs to be as

stable as possible; the knowledge base must be able to grow

and change, as knowledge is added; this arrangement enables the system to be

built from, or converted to, a shell.

EXPERT SYSTEM ARCHITECTURE (2)

It is reasonable to produce a richer, more elaborate, description of the typical expert system.

A more elaborate description, which still includes the components that are to be found in almost any real-world system, would look like this:

EXPERT SYSTEM ARCHITECTURE (2)

EXPERT SYSTEM ARCHITECTURE (2)

EXPERT SYSTEM ARCHITECTURE (2)

The system holds a collection of general principles which can potentially be applied to any problem - these are stored in the knowledge base.

The system also holds a collection of specific details that apply to the current problem (including details of how the current reasoning process is progressing) - these are held in working memory.

Both these sorts of information are processed by the inference engine.

EXPERT SYSTEM ARCHITECTURE (2)

Any practical expert system needs an explanatory facility. It is essential that an expert system should be able to explain its reasoning.

EXPERT & KNOWLEDGE-BASED SYSTEMS

One of AI’s greatest areas of success was the development of large-scale problem solving systems Originally called expert systems, they would mimic the

problem solving processes of domain experts Such as doctors performing diagnosis, or engineers performing

design, or wall street analysts selecting stock transactions Expert systems were originally developed by hand

And most commonly in some Lisp dialect It was discovered that many problems were being

solved by chaining through rules (if-then statements) that would operate on a collection of facts and partial conclusions Called working memory

These rule-based systems led to the first AI tools or shells Today, to simplify expert system creation, most people use

these AI shells – you just fill in the knowledge, the problem solving processes are already implemented

INTRODUCTION: DENDRAL The Dendral system (DENDRitic ALgorithm)

was the first expert system, developed in the 1960s The idea was, given mass spectrogram data,

determine what the chemical composition was The approach: plan-generate-and-test with

human feedback This is a constrained search technique

Generate a hypothesis: a possible chemical compound Test the hypothesis: use a series of heuristics and

subprograms to determine if the chemical compound generated is plausible given the data

If so, show it to the user – the user can steer the direction that DENDRAL takes next by suggesting what chemical elements should be added or removed from the generated hypothesis compound

DENDRAL repeats until an answer is found acceptable that does not violate the constraints as dictated by the data

PATTERN MATCHING Expert Systems consisted of two major components

A knowledge base The knowledge that the system would use to come to

conclusions In Dendral, this would be chemical elements, compounds, the mass

spectrogram data that could arise from any element In Mycin, this would be bacteriological infection knowledge

An inference engine How to apply the knowledge

For Dendral, this was done by generate-and-test, a very weak form of search

Mycin would perform backward chaining through rules More generically though, the inference engine would

embody some form of pattern matching One of the first systems that clearly demonstrated pattern

matching was Eliza Eliza was a spoof of an expert system but the code will show us

how pattern matching works

ELIZA Created by Joseph

Weizenbaum This system would act

like a Rogerian psychoanalyst by holding a dialog with a person the dialog would be

consist of the doctor (Eliza) asking questions, the human responding, and the doctor using the response to ask another question

Weizenbaum wrote the program so that it would generate an English response/question based on a group of patterns If the user sentence

matched a pattern, this pattern would be used to generate the next sentence/question

Sample dialog: H: Men are all alike E: In what way? H: They’re always bugging us about

something or another. E: Can you be more specific? H: He says I’m depressed much of

the time. E: I am sorry to hear you are

depressed. H: It’s true. I am unhappy. E: Do you think coming here will

help you not to be unhappy? H: I need some help, that much

seems certain. E: What would it mean to you if you

got some help? H: Perhaps I could learn to get along

with my mother. E: Tell me more about your family

HOW ELIZA WORKS Repeat

Input a sentence Find a rule in the Eliza knowledge-base that

matches the pattern Attempt to perform pattern match (see next slide) Attempt to perform segment match (see two slides)

If rule found, select one of the responses randomly (each pattern will have at least one response)

Fill in any variables Substitute values (you for I, I for you, me for you,

am for are, etc) Respond

Until user quits Most of the work goes on in the pattern

match and segment match functions

ARTIFICIAL INTELLIGENCE SYSTEMS

It falls into three basic categories: Expert systems. Natural Language Processing Perception Systems.

AI is a software technique applied to the non-numeric data expressed in terms of symbols, statements and patterns.

It uses methods of symbolic processing, social and scientific reasoning and conceptual modeling for solving the problems.

CATEGORIES OF AI

Artificial Intelligence System

Natural Language

Native Language Knowledge

Language Reasoning

Expert

Knowledge

Human Like Reasoning

Perception

Size, Shape, Image, Voice

Sensing Abilities for Reasoning

Uses

Applies

Uses

Applies

Uses

Applies

AI Applications

Uses Human Information Processing Capability

Uses Computer Intelligence for

producing Human Like

Capacity

Uses Human capabilities in

speech recognition,

Multi Sensory Interfacing

AI Applications

Intelligent AgentsFuzzy Logic

Learning SystemExpert System

Robotics Applications

Robot Systems for doing Human

Jobs

Natural Interface Applications

VR Systems

KNOWLEDGE BASED EXPERT SYSTEMS Decision making or problem solving is a

unique situation riddled with uncertainty and complexity, dominated by resource constraints and a possibility of several goals. In such cases, flexible systems (open systems) are required to solve the problems.

Most of such situations, termed as the unstructured situations, adopt two methods of problem solving, generalized or the knowledge based expert systems.

KBES

To build a KBES, certain prerequisites are required. The first prerequisite is that a person with the ability to solve the problem with knowledge based reasoning should be available.

Second prerequisite is that, such an expert should be able to articulate the knowledge to the specific problem characteristics.

Knowledge in KBES is defined as a mix of theory of the subject, knowledge of its application, organized information and the data of problems and its solutions.

REFERENCE

Waman Jawadekar, "Management Information Systems” , 4th Edition, Tata McGraw-Hill Publishing Company Limited.

E. Turban, J. Aronson, T.P. Liang, R. Sharda, “Decision Support and Business Intelligence Systems”, 8th Edition, Pearson Education.