lect01.pdf

Lecture Note for 300368 Intelligent Systems c!UWS (Yan Zhang) 1

Lecture One: Introduction to Artificial Intelligence

1. What are AI and Intelligent Systems

2. Foundation of AI

3. History of AI Research

4. Overview of AI and Intelligent Systems

5. Tutorial and Lab Questions


1 What are AI and Intelligent Systems?

Systems that can do something intelligently

ThinkingActing

Humans

Rationality


AI Systems:

Think like humans

Think rationally

Real World:

Act like humans

Act rationally


• Acting Humanly

AI systems (intelligent systems):

- Understanding natural language

- Reasoning

- Learning

- Vision

- Motion


But, what is the fundamental issue?

System


Turing Test

Screen 1 Screen 2Room 1 Room 2

womanprogram

interrogator


• Interrogator can input questions from computer;

• Computer in Room 1 can receive the question, and answer thequestion, display its answer on Screen 1;

• Computer in Room 2 can receive the question. The woman types heranswer into the computer in Room 2, and the computer then displaysthe woman’s answer on Screen 2;

• The woman in Room 2 always answers the question Truthfully;

• The computer in Room 1 can lie!


Interrogator: question 1, question 2, · · · question k;

Computer (Room 1): answer 1, answer 2, · · ·, answer k;

Woman (Room 2): answer’ 1, answer’ 2, · · ·, answer’ k.

Will the interrogator know which answers from computer and which answersfrom the woman?

If the interrogator cannot distinguish, then we say that the program in thecomputer (in Room 1) passes the Turing Test.

Question: Does such program exist?

The answer is Yes!


• Thinking humanly

Program

thinks like a human

How do humans think?

Cognitive science: Computer Science (AI) and Psychology


Psychology Theory abouthuman’s mind

Computermodel

Thinking rationally

Formal logic =! Computer program =! Reasoning

“Socrates is a man; all man are mortal; therefore Socrates is mortal”.


• Acting rationally

Beliefs Goal

inference?

Beliefs Goal

inference + rationality


2 Foundations of AI

AI

Computer

Psychology (1879 )

Engineering (1940 )

Mathematics (C. 800 )

Philosophy (428 B.C. )

(1957 )Linguistics


• Philosophy

Plato (428 B.C.)

Socrates (469 B.C. - 399 B.C.):

Logic of Mind =! Reasoning Procedure

Leibniz (1646 - 1716)

Mechanical Device+

Physical Laws

MentalOperations


Mind PhysicalDevice

Resoning+

KnowledgeSet of Laws

?

Knowledge Acquisition

EmpiricismInductionObservation· · ·


Connection between knowledge and action

Knowledge(beliefs) Goal

action?


• Mathematics

AI Programs

Logic ProbabilityComputation

Computation

Formal AlgorithmsDecidabilityIntractabilityNP - Complete


Logic

RepresentationReasoning

Probability

UncertaintyPossibility · · ·


• Psychology

Behaviourism

study mental constructs(knowledge, beliefs, goals, · · ·)

by using experiment approaches(stimulus - response)


Cognitive Psychology

How brain possesses and processes information?

Stimulus - Response:

stimulus =! internal representationrepresentation =! new internal representation

cognitive process:

new internal representation =! action (response)


• Computer Engineering

The first operational computer called Heath Robinson, built by Alan Turing(1940)

The first operational programmable computer called Z-3, Konard Zuse (1941)

IBM 701 built in 1952

AI’s contributions:

time sharing, interactive interpreters, personal computers withwindows and mice, link list data type, functional programming, etc.


• Linguistics

Language learning

Language understanding

Formal description of language

+ AI

or called

Computationallinguistics

nature languageprocessing

Modern linguistics


3 History of AI Research

• Artificial Intelligence - A new field

Warren McCulloch & Walter Pitts (1943) work on Neural Network

function of neurons in brain

propositional logic

Turing’s computation theory


Any computable function can be computed by some neural network

All propositional logical connections (",#,¬) can be implemented by simplenet structures

Marvin Minsky & Dean Eclmonds’ work (1959):

First neural network computer

Dartmouth workshop (1956):

- organized by John McCarthy- worked on automata theory, neural net, and intelligence

Result: A new field: Artificial Intelligence


• Early Research on AI

Newell & Simon’s GPS (General Problem Solver):GPS embodied the heuristic of means - ends analysis - a “thinking humanly”approach

Means Ends

StartGoal


John McCarthy (MIT, 1958):

Proposed LispInvented Time-Sharing =! DECProposed Advice Taker - the first complete AI systemProblem Solving & Knowledge RepresentationVision, LearningNatural Language Understanding

B

C

B

C

A

A

Block World


• Where is the difficulty?

Herbert Simon’s prediction in 1958:

- A computer would be chess champion in 10 years

- An important new mathematical theorem would be proved by machine

- Machines that think, learn, and create already exist


Difficulty 1:

Early AI systems lack knowledge of the subject matter, just simplesyntactic manipulations, e.g. machine translation

Difficulty 2:

Intractability of the problems - computationally infeasible

Difficulty 3:

In early AI systems, some fundamental limitations on the basicstructures were used to generate intelligent behaviour


• Knowledge-based Systems

General-purpose Problem Solvers (1958-1968)

Expert Systems (1969-1979)

Problem solving in specific area (domain knowledge):e.g. MYCIN - diagnose blood infectionsLUNAR - geology· · ·


• Recent Events (1987 - present)

AI adopts the scientific method (1987 - present)

Speech RecognitionPlanning and RoboticsProbability and Decision Theory in AIMachine Learning and knowledge DiscoveryGame PlayingKnowledge RepresentationComputer VisionMachine Translation


4 Overview of AI and Intelligent SystemsAI

AI Foundations AI Systems

Search KnowledgeRepresentation

Learning RoboticsPlanning

... ...

Systems ProcessingNatural LanguageKnowledge Based

...


• Intelligent Action

A Simple Example:Peter is planning to buy Christmas gifts for his Mother, Father, and Wife.Task 1. What to buy?Task 2. How to buy?

What to buy?

- since Peter’s mum likes plants, Peter decides to buy a plant for her

- since Peter’s father is learning to photography, Peter decides to buy aphotograph book for him

- since Peter’s wife is likely to enjoy a specific perfume, Peter decides tobuy it for his wife


Question: Suppose we want a robot to complete these tasks, how torepresent this information in machine?

Peter’s decision for buying gifts for his parents and wife come from hisknowledge about his parents and wife’s favour

How to encode such knowledge into a machine?

=! Knowledge Representation & Reasoning, which is not just aprogramming task!


How to get these gifts?

Peter’sHome

Plant Shop

Shopping Mall(Bookstore, Plant Shop)

Chemist Bookstore

Which is the best way to buy these three gifts?=! Search Problem!


• Search

Start

Goal2

2

1.5

3

3 3.2

2

3

2.8

2

Find the shortest path from start to goal


• Knowledge Representation

A: It is raining.B: The road is wet.A ! B: If it is raining, the road is wet.A, A ! BConclude: B due to modus ponens - an inference rule

Intelligent Action = Knowledge Representation + Search


• AI Systems

IntelligentAction

Planning

Learning

KBS...

Planning System: Generate actions to reach a goal:

Actions

Goal


Learning System (knowledge discovery)

LearningObservations

ConceptualKnowledge

KBS: Expert Systems, Intelligent Systems


5 Tutorial and Lab Questions

(1) Question 1.14 on page 32.

(2) Question 2.9 on page 63 (this is a programming task).

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