Artificial Intelligence

CIS 342

The College of Saint RoseDavid Goldschmidt, Ph.D.

I, RobotI, Robot by Isaac Asimov

What do we learn about robots?– Can “see” as humans do (computer vision)– Can walk and traverse around objects– Use intuition; are skeptical– Rely on reason– Are able to explain reasoning– Are curious as to one’s own existence– Are introspective

I, RobotI, Robot by Isaac Asimov

Is any of the AI in I, Robot realistic?– Computer vision– Robotics (boundary detection, learning)– Knowledge-based reasoning

Computer reasons and explains how aconclusion has been reached

– Expert systems Computer explains how a diagnosis

has been determined

Expert Systems

An expert system performs at a human expert level in a narrow and specialized domain– Often a tradeoff between accuracy and speed

Use symbolic reasoning to solve problems– Symbols represent facts and rules (i.e.

knowledge)– Symbols are usually human-readable as

opposed to cryptic variable names, etc.

Expert Systems

Expert systems apply heuristics to guidethe reasoning process– This reduces the search space and time

required toproduce one or more solutions

– The goal is not always tosolve the problem exactly;often just to identifyone or more good solutions

Expert Systems

Expert systems provide explanation facilities to display reasoning (e.g. applied rules) to users– How did you come to that conclusion or

diagnosis?

Expert systems make mistakes– So do human experts!– Users have to be aware

of this possibility

By Comparison....

Traditional programs

Human experts Expert systems

Process data and use algorithms to solve general numerical problems

Use knowledge in the form of “rules of thumb” or heuristics to solve problems in a narrow domain

Process knowledge expressed in the form of rules and use symbolic reasoning to solve problems in a narrow domain

By Comparison....

Traditional programs

Human experts Expert systems

Do not separate knowledge from the control structures and code used to process such knowledge

Have human brains; how does knowledge exist in the brain?

Provide a clear separation of knowledge and knowledge processing (reasoning)

By Comparison....

Traditional programs

Human experts Expert systems

Do not explain how particular results are obtained and why input data is needed

Are usually capable of explaining their line of reasoning and providing relevant details

Trace the rules fired during a problem-solving session; explain how a particular conclusion was reached and why specific input data was needed

By Comparison....

Traditional programs

Human experts Expert systems

Work only on problems where data is complete and exact

Use inexact reasoning and can deal with incomplete, uncertain, and fuzzy information

Permit inexact reasoning and can deal with incomplete, uncertain, and fuzzy information

By Comparison....

Traditional programs

Human experts Expert systems

Provide no solution at all (or an incorrect solution) when data is incomplete or fuzzy

Can make mistakes when information is incomplete of fuzzy

Can often deal with incomplete or fuzzy information; however, can also make mistakes

By Comparison....

Traditional programs

Human experts Expert systems

Enhance the quality of problem solving by changing the program code, which affects both the knowledge and its processing

Enhance the quality of problem solving via years of learning and personal training

Enhance the quality of problem solving by adding new rules or adjusting existing rules in a knowledge base

Knowledge

Knowledge is a theoretical or practical understanding of a subject or a domain– Those who possess knowledge are

called experts (or domain experts)– Experts have deep knowledge of facts, rules,

heuristics, etc.

In-Class Exercise

– Using your favorite programming language, create anexpert system to interactively ask users yes/no questions and diagnose one of the following health problems:

Cold and Flu Headaches Shoulder Problems Tooth Problems

– i.e. implement an expert system using charts from http://familydoctor.org/symptom.xml

Medical Diagnostics

from Every Patient Tells a Storyby Lisa Sanders, M.D.

ISBN 0767922476

Attempts to build expert systems formedical diagnosis go back to the mid-1970s– Peter Szolovits (Professor of Computer

Science at MIT)– Began his quest in 1976 (Caltech)– “We thought we could identify all of

the best practices in medicine, create a system that would make diagnosis faster and easier, and bring it all to doctors via a computer.”

Medical Diagnostics

from Every Patient Tells a Storyby Lisa Sanders, M.D.

ISBN 0767922476

“It’s simply not possible.”

“The emphasis and attention has shifted towardbringing below-average doctors up to current

standardsand helping even good doctors avoid doing

really stupid things.That turns out to provide greater benefits to

patients.”

DXplain

from Every Patient Tells a Storyby Lisa Sanders, M.D.

ISBN 0767922476

DXplain first launched in 1986– Users enter clinical information, then ask

DXplain to provide diagnostic decision support

– DXplain knowledge base (KB) covers ~2400 diseasesand over 5000 clinical findings (signs, symptoms, epidemiologic data, laboratory findings, etc.)

– Demo: http://dxplain.mgh.harvard.edu/dxp/dxp.sdemo.pl

– Info: http://lcs.mgh.harvard.edu/projects/dxplain.html

GIDEON

from Every Patient Tells a Storyby Lisa Sanders, M.D.

ISBN 0767922476

Global Infectious Disease and Epidemiology Network– Online application that supports the

diagnosis and treatment of infectious diseases

– Organized by country of origin– Updated weekly!– Info: http://www.gideononline.com/

Isabel

from Every Patient Tells a Storyby Lisa Sanders, M.D.

ISBN 0767922476

Second-generation diagnostic supportsystem with improved user interface– Freeform text entry– Modern search techniques

for text-based matching

– Info: http://www.isabelhealthcare.com/home/

Facts and Rules

A fact may be thought of as a unit of knowledge

A rule enables an artificially intelligent system to derive new facts from existing facts– Rules typically take the form of IF-THEN

statements– IF is the antecedent, premise, or condition– THEN is the consequent, conclusion, or action

Categories of Rules

Relation:IF the ‘fuel gauge’ is emptyTHEN the car is dead

Categories of Rules

Recommendation:IF the forecast is rainAND the sky is cloudyTHEN the advice is ‘bring your

umbrella’

Categories of Rules

Directive:IF the car is deadAND the ‘fuel gauge’ is emptyTHEN the action is ‘refuel the car’

Categories of Rules

Strategy:IF the car is deadTHEN the action is ‘check the fuel

gauge’;step 1 is complete

IF step 1 is completeAND the car is deadTHEN the action is ‘check the battery’;

step 2 is complete

Categories of Rules

Heuristic:IF the spill is liquidAND the ‘spill pH’ < 6AND the ‘spill smell’ is vinegarTHEN the ‘spill material’ is ‘acetic

acid’

Development Team

Expert System

End-user

Knowledge Engineer ProgrammerDomain Expert

Project Manager

Expert SystemDevelopment Team

Rule-Based Expert System

Newell and Simon, CMU (1970s)

Conclusion

REASONING

Long-term Memory

ProductionRule

Short-term Memory

Fact

Rule-Based Expert System

Inference Engine

Knowledge Base

Rule: IF-THEN

Database

Fact

Explanation Facilities

User Interface

User

Rule-Based Expert System

User

ExternalDatabase External Program

Inference Engine

Knowledge Base

Rule: IF-THEN

Database

Fact

Explanation Facilities

User Interface DeveloperInterface

Expert System

Expert

Knowledge Engineer

Inference Engine

New facts are discovered by anexpert system’s inference engine– The process is called inference – New facts are inferred – Facts serve as data within the knowledge

base– Rules (IF-THEN) describe how new data is

inferred

Inference Engine

Inference engine algorithm:– Inference engine compares each rule

with facts it already “knows” about,matching the antecedent (IF condition)

– When the antecedent matches one ormore known facts, the rule fires andits consequent (THEN) is executed

Inference Engine

Knowledge Base

Database

Fact: A is x

Match Fire

Fact: B is y

Rule: IF A is x THEN B is y

1

2 3

4

Inference Chain

An inference chain indicates how an expert system applies rules to reach a conclusionRule1: IF Y is true

AND D is trueTHEN Z is true

Rule2: IF X is trueAND B is trueAND E is trueTHEN Y is true

Rule3: IF A is trueTHEN X is true

A X

B

E

Y

D

Z

given: A, B, D, E

Inference Chain

An inference chain indicates how an expert system applies rules to reach a conclusionRule1: IF Y is true

AND D is trueTHEN Z is true

Rule2: IF X is trueAND B is trueAND E is trueTHEN Y is true

Rule3: IF A is trueTHEN X is true

A X

B

E

Y

D

Z

given: A, B, D, E

Inference Chain

An inference chain indicates how an expert system applies rules to reach a conclusionRule1: IF Y is true

AND D is trueTHEN Z is true

Rule2: IF X is trueAND B is trueAND E is trueTHEN Y is true

Rule3: IF A is trueTHEN X is true

A X

B

E

Y

D

Z

given: A, B, D, E

Forward Chaining

Forward chaining applies rules to knowndata to achieve a desired goal– Also called data-driven reasoning– At each iteration, topmost rule is executed– When fired, a rule adds a new fact

to the database– This match-fire cycle stops when the goal

has been found or when no other rulescan be fired

Match Fire Match Fire Match Fire Match Fire

Knowledge Base

Database

A C E

X

Database

A C E

L

Database

A D

YL

B

X

Database

A D

ZY

B

LX

Cycle 1 Cycle 2 Cycle 3

X & B & E

ZY & D

LC

L & M

A X

N

X & B & E

ZY & D

LC

L & M

A X

N

X & B & E

ZY & D

LC

L & M

A X

N

X & B & E

ZY & D

LC

L & M

A X

N

Knowledge Base Knowledge Base Knowledge Base

X

C E C EB D B D

Y Y Y Y

Forward Chaining

Match Fire Match Fire Match Fire Match Fire

Knowledge Base

Database

A C E

X

Database

A C E

L

Database

A D

YL

B

X

Database

A D

ZY

B

LX

Cycle 1 Cycle 2 Cycle 3

X & B & E

ZY & D

LC

L & M

A X

N

X & B & E

ZY & D

LC

L & M

A X

N

X & B & E

ZY & D

LC

L & M

A X

N

X & B & E

ZY & D

LC

L & M

A X

N

Knowledge Base Knowledge Base Knowledge Base

X

C E C EB D B D

Y Y Y Y

Match Fire Match Fire Match Fire Match Fire

Knowledge Base

Database

A C E

X

Database

A C E

L

Database

A D

YL

B

X

Database

A D

ZY

B

LX

Cycle 1 Cycle 2 Cycle 3

X & B & E

ZY & D

LC

L & M

A X

N

X & B & E

ZY & D

LC

L & M

A X

N

X & B & E

ZY & D

LC

L & M

A X

N

X & B & E

ZY & D

LC

L & M

A X

N

Knowledge Base Knowledge Base Knowledge Base

X

C E C EB D B D

Y Y Y Y

Match Fire Match Fire Match Fire Match Fire

Knowledge Base

Database

A C E

X

Database

A C E

L

Database

A D

YL

B

X

Database

A D

ZY

B

LX

Cycle 1 Cycle 2 Cycle 3

X & B & E

ZY & D

LC

L & M

A X

N

X & B & E

ZY & D

LC

L & M

A X

N

X & B & E

ZY & D

LC

L & M

A X

N

X & B & E

ZY & D

LC

L & M

A X

N

Knowledge Base Knowledge Base Knowledge Base

X

C E C EB D B D

Y Y Y Y

Match Fire Match Fire Match Fire Match Fire

Knowledge Base

Database

A C E

X

Database

A C E

L

Database

A D

YL

B

X

Database

A D

ZY

B

LX

Cycle 1 Cycle 2 Cycle 3

X & B & E

ZY & D

LC

L & M

A X

N

X & B & E

ZY & D

LC

L & M

A X

N

X & B & E

ZY & D

LC

L & M

A X

N

X & B & E

ZY & D

LC

L & M

A X

N

Knowledge Base Knowledge Base Knowledge Base

X

C E C EB D B D

Y Y Y Y

Forward Chaining Examples

Facts:

Rules: A X Q T A YA & D Q Q R R S N L T Z

A B C D E Prove: Z

Use forward chaining to prove the following:

Forward Chaining Examples

Facts:

Rules fired: A X A YA & D Q Q R R S Q T T Z

A B C D E

SOLUTION

Proven:Z

A B C D E X Y Q R S T ZA B C D E X A B C D E X Y A B C D E X Y Q A B C D E X Y Q R A B C D E X Y Q R S A B C D E X Y Q R S T

Use forward chaining to prove the following:

Forward Chaining Examples

Facts:

Rules: A X Q T A YA & D Q Q R R S N L T Z

A B C D E Prove: L

Use forward chaining to prove the following:

Forward Chaining Examples

Facts:

Rules fired: A X A YA & D Q Q R R S Q T T Z

A B C D E X Y Q R S T Z

SOLUTION

Cannot prove:

– No more rules left to fire

L

Use forward chaining to prove the following:

Forward Chaining

Using forward chaining, many rules may be executed unnecessarily– Search space is large and ever-expanding– Inefficiently fires rules that do not lead to

desired goal

Therefore, often prefer to use backward chaining....

Backward Chaining

Backward chaining starts with a desired goal,then searches for evidence to prove the goal

– Desired goal often calledthe hypothetical solution

– Backward chaining alsocalled goal-driven reasoning

Match Fire

Knowledge Base

Database

AB CD E

X

Match Fire

Knowledge Base

Database

AC DE

YX

B

Sub-Goal: X Sub-Goal: Y

Knowledge Base

Database

AC DE

ZY

B

X

Match Fire

Goal: Z

Pass 2

Knowledge Base

Goal: Z

Knowledge Base

Sub-Goal: Y

Knowledge Base

Sub-Goal: X

Pass 1 Pass 3

Pass 5Pass 4 Pass 6

Database

AB CD E

Database

AB CD E

Database

BC DEA

YZ

?

X

?

X & B & E

LCL & M

A X

N

ZY & DX & B & EY

ZY & D

LCL & M

A X

NLC

L & M N

X & B & EYZY & D

A X

X & B & EYZY & D

LC

L & M

A X

N

X & B & E

LCL & M

A X

N

ZY & DX & B & E

ZY & D

LC

L & M

A X

N

Y

YY

Backward Chaining

Backward chaining algorithm:– At the first iteration, rule(s) with the

desired goal in the consequent are selected– Stack up and attain many subgoals until....– If the antecedent matches known data,

the rule is fired and the goal is proven– Otherwise, if no rules remain,

the desired goal is not proven

Match Fire

Knowledge Base

Database

AB CD E

X

Match Fire

Knowledge Base

Database

AC DE

YX

B

Sub-Goal: X Sub-Goal: Y

Knowledge Base

Database

AC DE

ZY

B

X

Match Fire

Goal: Z

Pass 2

Knowledge Base

Goal: Z

Knowledge Base

Sub-Goal: Y

Knowledge Base

Sub-Goal: X

Pass 1 Pass 3

Pass 5Pass 4 Pass 6

Database

AB CD E

Database

AB CD E

Database

BC DEA

YZ

?

X

?

X & B & E

LCL & M

A X

N

ZY & DX & B & EY

ZY & D

LCL & M

A X

NLC

L & M N

X & B & EYZY & D

A X

X & B & EYZY & D

LC

L & M

A X

N

X & B & E

LCL & M

A X

N

ZY & DX & B & E

ZY & D

LC

L & M

A X

N

Y

YY

Backward Chaining

Match Fire

Knowledge Base

Database

AB CD E

X

Match Fire

Knowledge Base

Database

AC DE

YX

B

Sub-Goal: X Sub-Goal: Y

Knowledge Base

Database

AC DE

ZY

B

X

Match Fire

Goal: Z

Pass 2

Knowledge Base

Goal: Z

Knowledge Base

Sub-Goal: Y

Knowledge Base

Sub-Goal: X

Pass 1 Pass 3

Pass 5Pass 4 Pass 6

Database

AB CD E

Database

AB CD E

Database

BC DEA

YZ

?

X

?

X & B & E

LCL & M

A X

N

ZY & DX & B & EY

ZY & D

LCL & M

A X

NLC

L & M N

X & B & EYZY & D

A X

X & B & EYZY & D

LC

L & M

A X

N

X & B & E

LCL & M

A X

N

ZY & DX & B & E

ZY & D

LC

L & M

A X

N

Y

YY

Match Fire

Knowledge Base

Database

AB CD E

X

Match Fire

Knowledge Base

Database

AC DE

YX

B

Sub-Goal: X Sub-Goal: Y

Knowledge Base

Database

AC DE

ZY

B

X

Match Fire

Goal: Z

Pass 2

Knowledge Base

Goal: Z

Knowledge Base

Sub-Goal: Y

Knowledge Base

Sub-Goal: X

Pass 1 Pass 3

Pass 5Pass 4 Pass 6

Database

AB CD E

Database

AB CD E

Database

BC DEA

YZ

?

X

?

X & B & E

LCL & M

A X

N

ZY & DX & B & EY

ZY & D

LCL & M

A X

NLC

L & M N

X & B & EYZY & D

A X

X & B & EYZY & D

LC

L & M

A X

N

X & B & E

LCL & M

A X

N

ZY & DX & B & E

ZY & D

LC

L & M

A X

N

Y

YY

Match Fire

Knowledge Base

Database

AB CD E

X

Match Fire

Knowledge Base

Database

AC DE

YX

B

Sub-Goal: X Sub-Goal: Y

Knowledge Base

Database

AC DE

ZY

B

X

Match Fire

Goal: Z

Pass 2

Knowledge Base

Goal: Z

Knowledge Base

Sub-Goal: Y

Knowledge Base

Sub-Goal: X

Pass 1 Pass 3

Pass 5Pass 4 Pass 6

Database

AB CD E

Database

AB CD E

Database

BC DEA

YZ

?

X

?

X & B & E

LCL & M

A X

N

ZY & DX & B & EY

ZY & D

LCL & M

A X

NLC

L & M N

X & B & EYZY & D

A X

X & B & EYZY & D

LC

L & M

A X

N

X & B & E

LCL & M

A X

N

ZY & DX & B & E

ZY & D

LC

L & M

A X

N

Y

YY

Backward Chaining

Match Fire

Knowledge Base

Database

AB CD E

X

Match Fire

Knowledge Base

Database

AC DE

YX

B

Sub-Goal: X Sub-Goal: Y

Knowledge Base

Database

AC DE

ZY

B

X

Match Fire

Goal: Z

Pass 2

Knowledge Base

Goal: Z

Knowledge Base

Sub-Goal: Y

Knowledge Base

Sub-Goal: X

Pass 1 Pass 3

Pass 5Pass 4 Pass 6

Database

AB CD E

Database

AB CD E

Database

BC DEA

YZ

?

X

?

X & B & E

LCL & M

A X

N

ZY & DX & B & EY

ZY & D

LCL & M

A X

NLC

L & M N

X & B & EYZY & D

A X

X & B & EYZY & D

LC

L & M

A X

N

X & B & E

LCL & M

A X

N

ZY & DX & B & E

ZY & D

LC

L & M

A X

N

Y

YY

Match Fire

Knowledge Base

Database

AB CD E

X

Match Fire

Knowledge Base

Database

AC DE

YX

B

Sub-Goal: X Sub-Goal: Y

Knowledge Base

Database

AC DE

ZY

B

X

Match Fire

Goal: Z

Pass 2

Knowledge Base

Goal: Z

Knowledge Base

Sub-Goal: Y

Knowledge Base

Sub-Goal: X

Pass 1 Pass 3

Pass 5Pass 4 Pass 6

Database

AB CD E

Database

AB CD E

Database

BC DEA

YZ

?

X

?

X & B & E

LCL & M

A X

N

ZY & DX & B & EY

ZY & D

LCL & M

A X

NLC

L & M N

X & B & EYZY & D

A X

X & B & EYZY & D

LC

L & M

A X

N

X & B & E

LCL & M

A X

N

ZY & DX & B & E

ZY & D

LC

L & M

A X

N

Y

YY

Backward Chaining Examples

Facts:

Rules: A X Q T A YA & D Q Q R R S N L T Z

A B C D E Prove: Z

Use backward chaining to prove the following:

Backward Chaining Examples

Facts:

Stack of rules: (subgoals)

Rules fired: A & D Q Q T T Z

A B C D E Q T Z

SOLUTION

Proven:ZA & D Q Q T T Z

A B C D E

Use backward chaining to prove the following:

Backward Chaining Examples

Facts:

Rules: A X Q T A YA & D Q Q R R S N L T Z

A B C D E Prove: L

Use backward chaining to prove the following:

Backward Chaining Examples

Facts:

Stack of rules: (subgoals)

Cannot prove:– Subgoal N cannot be proven

A B C D E

SOLUTION

N L

L

Use backward chaining to prove the following:

Conflict Resolution

Rules with identical antecedents (IF conditions) can cause conflicts via their consequents (THEN clauses)

Conflict resolution provides a specific method for choosing which rule to fire– Highest priority– Most specific rule– Most recent first