INFSY540 Information Resources in Management Lesson 9 Chapter 10 Artificial Intelligence & Expert Systems.

INFSY540Information Resources in

ManagementLesson 9

Chapter 10Artificial Intelligence & Expert Systems

Chapter 10Slide 2

Learning ObjectivesDefine “artificial intelligence” (AI)Identify the major types of AI systems & provide an example of each List the characteristics and basic components of expert systemsIdentify at least 3 factors to consider in evaluating the development of an expert systemOutline & explain the steps in developing an expert system

Chapter 10Slide 3

How do AI persons think?

What is AI?

What is I?

Chapter 10Slide 4

Characteristics of Intelligence

Ability to Communicate

Creativity

Internal Knowledge

Ability to Learn

World Knowledge

Goal-Directed Behavior

Self Awareness

Chapter 10Slide 5

A Hierarchical Model of Intelligence

Wisdom

Knowledge

Information

Data Context+

Vision+Experience+

Chapter 10Slide 6

What is Artificial Intelligence?

Good Question. There is no generally accepted definition of Artificial Intelligence.

Why? In practice, it is an “umbrella term” It is multidisciplinaryTechnologies regularly enter and exit the AI

“umbrella”

Chapter 10Slide 7

AI is a Multi-Disciplinary FieldHistorically, AI practitioners

came from diverse backgrounds in both

“hard” and “soft” sciences.

CognitiveScience

Linguistics Engineering

Psychology

Artificial Intelligence

Computer Science

What other disciplines have been involved in AI?

Chapter 10Slide 8

Brief History of AI 1943 McCulloch & Pitts paper on neurons

1950 Age of computer simulation begins

1956 Cognitive AI & Neural Computing fields begin

(Dartmouth Summer Research Conference)

1957 Rosenblatt’s Perceptron

1959 Widrow & Hoff’s MADALINE

1960’s Growth, Progress and Excessive Hype in all of AI

1969 Minsky & Papert’s critique of Perceptrons

(Results in stunted growth of Neural Networks: 1969-1984)

1986 Re-birth of Neural Networks

1997 Deep Blue defeats reigning chess grandmaster

Chapter 10Slide 9

Turing’s Test

Can the human on the left tell whether the output iscoming from the computer or the human on the right?

Chapter 10Slide 10

Features of Artificial Intelligence

The use of computers to do symbolic reasoning

A focus on problems that do not respond to algorithmic

solutions

Problem solving using inexact, missing, or poorly defined

information

An effort to capture and manipulate the significant qualitative

features of a situation rather than relying on numerical

methods

Chapter 10Slide 11

Features of Artificial Intelligence

An attempt to deal with issues of semantic meaning as well

as syntactic form

Answers that are neither exact or optimal, but are in some

sense “sufficient”

The use of large amounts of domain-specific knowledge in

solving problems

The use of meta-level knowledge to effect more

sophisticated control of problem-solving strategies

Chapter 10Slide 12

Application CategoriesInterpretation Inferring situation from observations

Prediction Inferring likely consequences of situation

Diagnosis Inferring malfunctions

Design Configuring objects under constraints

Planning Developing plans to achieve goals

Monitoring Comparing observations to plans

Debugging Prescribing remedies for malfunctions

Repair Executing a plan to administer a remedy

Instruction Diagnosing and correcting performance

Control Managing system behavior

Optimization Finding “best” solutions to problems

Chapter 10Slide 13

Some AI Technologies

Expert Systems

Neural Networks

Genetic Algorithms

Fuzzy Logic

Robotics

Natural-Language Processing

Intelligent Tutorials

Computer Vision

Virtual Reality

Game Playing

Chapter 10Slide 14

Some AI TechnologiesExpert Systems: Diagnose, respond & act like a human expert

Neural Networks: Use data to predict outputs or interpret inputs

Genetic Algorithms: Use data to find “optimal” solutions

Fuzzy Logic: Facilitate solutions to human vagueness problems

Robotics: Mimic physical human processes

Natural-Language Processing: Mimic human communication

Intelligent Tutorials: Facilitate human learning

Computer Vision: Mimic human sensory(visual) process

Virtual Reality: Mimic human reality inside a computer

Game Playing: Beat humans in games, e.g. chess

Chapter 10Slide 15

Cognitive vs Biological AI

Cognitive-based Artificial Intelligence Top Down approachAttempts to model psychological processesConcentrates on what the brain gets done

Biological-based Artificial IntelligenceBottom Up approachAttempts to model biological processesConcentrates on how the brain works

Chapter 10Slide 16

Cognitive vs Biological AI

Cognitive AI Tools: Expert Systems Natural Language Fuzzy Logic Intelligent Agents Intelligent Tutorials Planning Systems Virtual Reality

Biological AI Tools Neural Networks Speech Recognition Computer Vision Genetic Algorithms Evolutionary

Programming Machine Learning Robotics

Chapter 10Slide 17

What is Artificial Intelligence?

Some definitions of AI: Eugene Charniak, “...the study of mental faculties through

the use of computational models.” Patrick Winston, “...the study of computations that make it

possible to perceive, reason, and act.” Steven Tanimoto, “...computational techniques for

performing tasks that apparently require intelligence when performed by humans.”

David Parnas, “Artificial intelligence is to artificial flowers as natural intelligence is to natural flowers.”

Chapter 10Slide 18

Categories of AI Definitions

Systems that:

Think like humans Think rationally

Act like humans Act rationally

Chapter 10Slide 19

What is Artificial Intelligence?Artificial Intelligence: the art of making computers that behave like the ones in movies”

Bill Bulko

Computers are useless. They can only give you answers. Pablo Picasso

Computers make it easier to do a lot things, but most of the things they make easier to do, don’t need to be done.

Andy Rooney

The question of whether a computer can think is no more interesting than the question of whether a submarine can swim. Edgar W. Dijkstra

Chapter 10Slide 20

Questions?Suppose we develop an AI program so that it can score 200 on a standard IQ test. Would we then have a program more intelligent than a human?

“Surely computers cannot be intelligent-they can only do what their programmers tell them.” Is the latter statement true and does it imply the former?

“Surely animals cannot be intelligent-they can only do what their genes tell them.” Is the latter statement true and does it imply the former?

Chapter 10Slide 21

Predicting the Future: Mission Impossible?

I think there’s a world market for about 5 computers. Thomas J. Watson, Chairman of the Board, IBM, 1948

There is no reason for any individual to have a computer in his home. Ken Olson, President, Digital Equipment, 1977

Chapter 10Slide 22

Future AI Technologies

Will need to do more than just mimic humans to improve computer intelligence. For example, examine products for defects under light and

sound frequencies that human experts cannot observe.

Will need to focus on creating computer programs that can learn and teach other computer programs.

Chapter 10Slide 23

Future AI Technologies

Automatic Programming

Evolutionary Programming

Knowledge Based Systems

Biological Artificial Neural Networks

Real Time Planning and Re-Planning Systems

Intelligent “learning” Agents

Micro, mini and nano robots

Biometric Security Systems

Quantum computing

Chapter 10Slide 24

Why Should We Care about AI?

Moving from the industrial age to the information age has created a whole new world of problems. There are many very difficult problems in this new world that an AI way of thinking might help solve. Information overload problems. Operations in hazardous environments. Distributing scarce corporate knowledge. Problems requiring multidisciplinary teams.

Chapter 10Slide 25

Any questions?

Knowledge Based Systems (KBS) and

Expert Systems (ES)

Chapter 10Slide 27

Expert System

A model and associated procedure that exhibits, within a specific domain, a degree of expertise in problem solving that is comparable to that of a human expert. (From Introduction to Expert Systems by Ignizio)

An expert system is a computer system which emulates the decision-making ability of a human expert. (From Expert Systems: Principles and Programming by Giarratano and Riley)

Problem solving programs that usually have an explanation facility and are rich in heuristics.

Chapter 10Slide 28

Characteristics of an Expert System

Can explain reasoning

Can provide portable knowledge

Can display “intelligent” behavior

Can draw conclusions from complex relationships

Can deal with uncertainty

Chapter 10Slide 29

What distinguishes a KBS from an expert system?

Size of the knowledge base

Reuse of the knowledge

Generality of the knowledge

Large-scale integrated architectures with multiple reasoning strategies

Chapter 10Slide 30

Preserve knowledge--builds up the corporate memory of an organization.

Makes expertise more widely available, even if scarce or expensive.

Frees expert from repetitive, routine tasks.

Aids in imparting expertise to novices.

Improves worker productivity.

Explore alternatives -- provides a second opinion in critical situations.

Why use a KBS or ES?

Chapter 10Slide 31

When to use a KBS or ES?

Domain is knowledge intensive, and can be modeled with logical rules

Not a natural-language intensive problem

Neither creativity nor physical skills are required

Optimal results are not required

Subject matter experts are available for knowledge acquisition

Chapter 10Slide 32

When to use a KBS or ES?

High payoff

Preserve scarce expertise

Distribute expertise

Provide more consistency than humans

Faster solutions than humans

Training expertise

Chapter 10Slide 40

Components of KBS and ES

EssentialKnowledge base Inference engine

SupportingKB editorQuery interface Explanation system

Chapter 10Slide 41

Fig 11.7

Chapter 10Slide 43

Inference Engine

Human reasoning inspires similar reasoning strategies in AI:ClassificationRulesHeuristicsPrior casesExpectations

Chapter 10Slide 44

Classification

We create and use categories to organize knowledge

Animal

Vertebrate Invertebrate

Fish

Reptile Amphibian Mammal

Chapter 10Slide 45

Rules

Mostly take the form IF-THEN

Rules can be cascaded, nested"If A then B" . . . "If B then C" A-->B-->C

Order of evaluation may matter

Chapter 10Slide 46

Heuristics

“Rules of thumb”

Heuristics can be captured using rules"If the meal includes red meat

Then choose red wine" If the TV reception is bad

Then jiggle the antenna

Can be extremely helpful in AI applications

Chapter 10Slide 47

Prior Cases

Exemplified in case-based reasoninge.g. legal precedents

Similarity of current case to previous cases provides basis for action choice

Cases stored and retrieved based on features and structure

Similarities and differences are the basis for reasoning

Chapter 10Slide 50

Inference Engine

Controls overall execution of the “rules”.

Descriptions of the StrategiesForward Chaining

Derive new facts from existing facts“Who killed the cat?”

Backward ChainingAsk if a particular hypothesis is valid. (Goal-directed

inference)“Did curiosity kill the cat?”

Can combine the strategies

Chapter 10Slide 53

Knowledge Base

Uses a representation language to formalize knowledge

Context: Organizes domain into a model of entities and relationships that make up that domain.

Rules: Logical statements that govern the inference about the entities and relationships attempt to replicate the thought process used by the expert. Two methods of designing the rules: Rule-Based

Reasoning and Case-Based Reasoning

Chapter 10Slide 54

Knowledge Base

Rule-based Reasoning Uses logical rules to guide inference.

1. If you are 150 yds. away and in the fairway, then select the 7-iron.

2. If you are in the rough, then use the next lower-numbered club.

If you start with (150yds, rough), then by applying the above two rules you will get 6-iron as output.

The rules operate on beliefs and assumptions in the reasoning context

Chapter 10Slide 55

Knowledge Base

Case-based Reasoning Look at all related facts as a “case”, seek to find

similar cases to guide inference Reason based on the similarities and differences. Example, 1st step, using same problem:

Case 1: 170 yds., in fairway; used a 5-iron. Case 2: 160 yds., in fairway; used a 6-iron. Case 3: 150 yds., in fairway; used a 7-iron.

(150 yds., rough) is probably closest to Case 3.

Chapter 10Slide 56

Knowledge Base

Case-based Reasoning (second step): Apply rules about what doesn’t match the case:

a. If the situation is “fairway” and the case is for “rough”, then use the next higher-numbered club.

b. If the situation is “rough” and the case is for “fairway”, then use the next lower-numbered club.

Since the situation is “rough” and Case 3 (the best matching case) is for “fairway”, we would apply the b. rule above to derive our answer of 6-iron.

Chapter 10Slide 57

Knowledge Base

Rule-Based and Case-Based Reasoning are equivalent: Any rule-based system can be rewritten in case-based form, and vice versa.

Using one over the other depends on how the experts do their job: Rule-based: Do they look at one piece of data

at a time? Case-based: Do they generally reason about

the data in a “big picture” way?

Chapter 10Slide 60

Applications of Expert Systems & KBS

Credit granting

Shipping

Information management & retrieval

Embedded systems

Help desks & assistance

Chapter 10Slide 61

Application Categories:Interpretation

Urban Search and Rescue robots Interprets information about collapsed buildings Helps identify potential locations of trapped

victims. ES is programmed into the robot exploring the

inside of the building looking for “void spaces”.Colorado School of Mines

Chapter 10Slide 62

Application Categories:Interpretation

Bridge ClassificationThe “Smart Bridge” project allows planners to

classify bridges according to capacity:Load Classification (weight, throughput,...)Clearance Restrictions

Operates using remote imagery (photographs, satellite images)

Chapter 10Slide 63

Application Categories:Diagnosis & Repair

Turbine Engine Vibration DiagnosisTakes acoustic spectrum from a running a

turbine engine. Irregular components of the signal patterns are

identified. Mechanic is pointed towards possible faults.

Chapter 10Slide 64

The US Army AI Center’s Favorite Photo

The single locked box at the soldier’s feet replaces the stack ofmanuals and the tower of test equipment shown.

Chapter 10Slide 71

Limitations of Knowledge Based SystemsLimited to narrow problems

Not widely used or tested

Hard to use

Cannot easily deal with “mixed” knowledge

Possibility of error

Cannot refine own knowledge base

Hard to maintain

Possible high development costs

Raise legal & ethical concerns

Chapter 10Slide 72

Advantages of Expert Systems Shells and ProductsEasy to develop & modify

Use of satisficing

Use of heuristics

Development by knowledge engineers & users

Chapter 10Slide 73

Procedural Computing

Conventional software programming paradigm relies on procedural computing over data:Program = Algorithm + Data

Algorithm is a series of tasks that the computer must perform, such as:

read a numbermultiply by 10display the resultetc…

Chapter 10Slide 74

How Do Expert Systems Differ from Conventional Programs?

As a model of human cognition?

From a programming perspective?

In their performance?

Ability to provide justification?

Relationship to expert behavior?

Are expert systems intelligent?