infsy540_Lsn9_AI&ES

blabbedharborAI and Robotics

Feb 23, 2014 (3 years and 8 months ago)

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INFSY540

Information Resources in
Management

Lesson 9


Chapter 10

Artificial Intelligence & Expert Systems


Chapter 10

Slide
2

Learning Objectives

Define “artificial intelligence” (AI)

Identify the major types of AI systems &
provide an example of each

List the characteristics and basic components
of expert systems

Identify at least 3 factors to consider in
evaluating the development of an expert
system

Outline & explain the steps in developing an
expert system

Chapter 10

Slide
3

How do AI persons think?


What is AI?


What is I?

Chapter 10

Slide
4

Characteristics of
Intelligence

Ability to Communicate

Creativity

Internal Knowledge

Ability to Learn

World Knowledge

Goal
-
Directed Behavior

Self Awareness

Chapter 10

Slide
5

A Hierarchical Model of
Intelligence

Wisdom

Knowledge

Information

Data

Context

+

Vision

+

Experience

+

Chapter 10

Slide
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 multidisciplinary


Technologies regularly enter and exit the AI
“umbrella”

Chapter 10

Slide
7

AI is a Multi
-
Disciplinary Field

Historically, AI practitioners
came from diverse
backgrounds in both
“hard” and “soft” sciences
.




Cognitive

Science

Linguistics

Engineering

Psychology

Artificial

Intelligence

Computer

Science

What other
disciplines have
been involved in AI?

Chapter 10

Slide
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 10

Slide
9

Turing’s Test

Can the human on the left tell whether the output is

coming from the computer or the human on the right?

Chapter 10

Slide
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 10

Slide
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 10

Slide
12

Application Categories

Interpretation

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 10

Slide
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 10

Slide
14

Some AI Technologies

Expert 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 10

Slide
15

Cognitive
vs

Biological
AI

Cognitive
-
based Artificial Intelligence


Top Down approach


Attempts to model psychological processes


Concentrates on what the brain gets done


Biological
-
based Artificial Intelligence


Bottom Up approach


Attempts to model biological processes


Concentrates on how the brain works

Chapter 10

Slide
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 10

Slide
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 10

Slide
18

Categories of AI Definitions


Systems that:


Think like humans


Think rationally


Act like humans



Act
rationally

Chapter 10

Slide
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 10

Slide
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 10

Slide
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 10

Slide
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 10

Slide
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 10

Slide
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 10

Slide
25



Any questions?


Knowledge Based Systems (KBS)


and

Expert Systems (ES)

Chapter 10

Slide
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 10

Slide
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 10

Slide
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 10

Slide
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 10

Slide
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 10

Slide
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 10

Slide
40

Components of KBS and ES

Essential



Knowledge base


Inference engine

Supporting


KB editor


Query interface


Explanation system

Chapter 10

Slide
41

Fig 11.7


Chapter 10

Slide
43

Inference Engine

Human reasoning inspires similar
reasoning strategies in AI:


Classification


Rules


Heuristics


Prior cases


Expectations

Chapter 10

Slide
44

Classification

We create and use categories to
organize knowledge

Animal

Vertebrate

Invertebrate

Fish

Reptile

Amphibian

Mammal

Chapter 10

Slide
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 10

Slide
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 10

Slide
47

Prior Cases

Exemplified in case
-
based reasoning


e.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 10

Slide
50

Inference Engine

Controls overall execution of the “rules”.

Descriptions of the Strategies


Forward Chaining


Derive new facts from existing facts


“Who killed the cat?”


Backward Chaining


Ask if a particular hypothesis is valid.
(Goal
-
directed inference)


“Did
curiosity

kill the cat?”

Can combine the strategies


Chapter 10

Slide
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 10

Slide
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 10

Slide
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 10

Slide
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 10

Slide
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 10

Slide
60

Applications of
Expert Systems & KBS

Credit granting

Shipping

Information management & retrieval

Embedded systems

Help desks & assistance

Chapter 10

Slide
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 10

Slide
62

Application Categories:

Interpretation

Bridge Classification


The “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 10

Slide
63

Application Categories:

Diagnosis & Repair

Turbine Engine Vibration Diagnosis


Takes acoustic spectrum from a running a
turbine engine.


Irregular components of the signal patterns are
identified.


Mechanic is pointed towards possible faults.

Chapter 10

Slide
64

The US Army AI Center’s
Favorite Photo

The single locked box at the soldier’s feet
replaces

the stack of

manuals and the tower of test equipment shown.

Chapter 10

Slide
71

Limitations of
Knowledge Based Systems

Limited 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 10

Slide
72

Advantages of Expert
Systems Shells and Products

Easy to develop & modify

Use of satisficing

Use of heuristics

Development by knowledge engineers
& users

Chapter 10

Slide
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 number


multiply by 10


display the result


etc…

Chapter 10

Slide
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?