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))