APPLYING THE SYMBOLIC PARADIGM
GOOD OLD-FASHIONED ARTIFICIAL INTELLIGENCE (GOFAI)
AI as an expert system
MYCIN: designed to simulate a
human expert in diagnosing
infectious diseases
➢Produced an acceptable
diagnosis in 69 percent of cases
(higher than human experts)
SHRDLU
Written by Terry Winograd
The program was capable of using language to
report on its environment, to plan actions, and
to reason about the implications of what is
being said to it.
Programmed to deal with a very limited virtual
micro-world
EXPERT SYSTEMS AND DECISION TREES
A loan decision tree
➢A program that is capable of producing
its own decision tree?
➢ Starting from a huge database of all the
previous loan decisions
➢Organizing a decision tree from the
database and use it for new loan
applications – machine learning problem
MACHINE LEARNING
Machine learning algorithm is an instance of the physical symbol system
Job of the algorithm is to transform the complex database into a different kind of symbol structure – a set of IF … THEN … rules that collectively determine a decision tree.
The physical symbol system
(1) Symbols are physical patterns
(2) Symbols can be combined to form complex symbol structures
(3) The system contains processes for manipulating complex symbol structures
(4) The processes for representing complex symbol structures can themselves by symbolically represented
within the system
ID3
ID3 (ROSS QUINLAN)
Rule Quest Research : https://www.rulequest.com/index.html
© RULEQUEST RESEARCH 2018 Last updated July 2018
About us...RuleQuest Research is a small Australian company dedicated to the development of high-performance data
mining tools. Since it opened its doors in 1997, RuleQuest has won thousands of users in more than 50
countries with applications as diverse as geographic information systems (GIS), pharmaceutics, customer
relations management (CRM), and text analysis. Accounts of some of these are grouped under classification
(See5/C5.0) and predicting values (Cubist).
Ross Quinlan, RuleQuest's founder, has spent forty years working in Data Mining and Machine Learning and is
the author of popular data mining systems such as ID3, C4.5, and FOIL. He holds a PhD in Computer Science
from the University of Washington.
ID3: AN ALGORITHM FOR MACHINE LEARNING (ROSS QUINLAN)
ID3 (Machine Learning Algorithm) only works on databases that take a very specific form
Basic objects in the database: Examples, each example has attributes.
Examples: loan applicants
Attributes: credit history? Income? Each attribute has values
Credit history: good or bad
Income: high, middle, low
Target attribute: Loan, Values: Yes, No.
This kind of databases have three basic features
1. The examples must be characterizable in terms of a fixed set of attributes
2. Each attribute must have a fixed set of values
3. Every example has exactly one value for each attribute
ID3: FROM DATABASE TO DECISION TREE
Decide which attribute will be the most informative
at the node: how to measure informativeness?
Information Gain
Entropy: measure of uncertainty
Entropy vs. Probability
Q1. Explain in your own words why the graph is symmetrical
ENTROPY AND INFORMATION GAIN
( )Pr ( )
( )
YESYES N A
op AN S
=
Entropy Information Gain
ID3 IN ACTION
Target attribute: Play Tennis?
Other attributes
Outlook? {sunny, overcast, rain}
Temperature? {hot, mild, cool}
Humidity? {high, low, normal}
Wind? {weak, strong}
ID3 IN ACTION
Baseline entropy
ID3 IN ACTION
Calculation of information gain
ID3 IN ACTION
Decision tree
ID3 IN ACTION
Decision tree
Q4. Calculate the information gain for
temperature, humidity, and wind on
Sunny and Rain
HUMAN EXPERT VS. ID3
An expert system for diagnosing diseases in soybeans
19 common diseases
35 different symptoms
ID3 outperformed human experts: 99.5% success rate
vs. 87 % success rate
WHISPER
DIAGRAMMATIC REPRESENTATION
Mental rotation experiment
Imagistic representation vs. propositional
representation
Diagrammatic representation
WHISPER
The basic architecture
Heuristic search
Problem structure
Transformation of the previous diagram
Solution structure – all points are stable
WHISPER
Operation Perceptual primitives
WHISPER
Operation
Steps 1 ~ 3
WHISPER
Operation
Step 4
WHISPER
Operation
Step 5~6
WHISPER
Operation
Step 7
SHAKEY THE ROBOT
SHAKEY
SHAKEY
▪ Basic Idea: Complex behaviors are hierarchically organized
(Lashley’s argument: Complex behaviors resulted from prior
planning and organization).
▪ Hardware and software
▪ Level 1: Hardware, Level 2 – 5: Software
▪ Level 2: Low-level actions (LLAs)
▪ Level 3: Intermediate-level actions (ILAs)
LLAS
Execution of each LLA changes the robot’s state, so requires the
model to be updated
ILAS
▪ ILAs are action routines: linked
sequences of LLAs that SHAKEY can
call upon in order to execute specific
jobs.
▪ ILA can recruit other ILAs.
▪ Predicate calculus? (vs. propositional calculus)
STRIPS(STANDARD RESEARCH INSTITUTE PROBLEM SOLVER) &
PLANEX
▪ PLANEX: Error correction and adjustment of the plan
▪ When the likely degree of error reaches a certain threshold, PLANEX
instructs SHAKEY to take a photograph to check on its position.
Shakespear wrote Hamlet :
write(Shakespear, Hamlet)
Every piece of work written by Shakespear:
∀x (write(Shakespear, x)
Color of every elephant is gray:
(∀x) (elephant(x) => color(x, Gray))
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