artificial intelligence - shoib and ruth
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Artificial IntelligenceCOMP3702/COMP7702
Course Coordinator
Janet Wiles
Lecturers
Shoaib Sehgal
Ruth Schulz
Tutor
Suren Rathnayake
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Aims and Objectives
View: Methods and techniques within the field of
artificial intelligence and solve theoretical and practicalproblems.
Scope: The course provides an understanding of AI and
describes many of the most important algorithms andtechniques which are theoretcally pratically important.
Purpose: The course helps the student to:
gain an appreciation for the scientific context ofartificial intelligence
understand and develop computing algorithms, and toanalyse their properties
find the right techniques for solving specificproblems, and to implement these techniques
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Aims and Objectives (cont)
In general terms, it is expected that the student gains an
understanding of the theories, methods and practices whichform the basis of Artificial Intelligence.
The course aims to introduce the basic concepts andmethods used in the field of artificial intelligence and
provide students with skills in the use of applying thesetechniques.
Specifically the course aims to give students an overview ofthe following topics in artificial intelligence:
Problem solving and optimisation (search algorithms) Reasoning with uncertain knowledge (probability
theory)
Machine Learning (classification, etc)
Probabilistic approaches for information retrieval
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Aims and Objectives (cont)
After the course, you should
be familiar with the historical context of artificialintelligence
know several definitions of artificial intelligence
be familiar with an agent-based intelligent system design understand several problem solving and optimisation
techniques based on search (both uninformed andinformed)
be able to implement and apply search techniques
understand general principles of machine learning (bothsupervised and unsupervised)
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Aims and Objectives (cont)
In addition, you should
know several machine learning techniques(including decision tree learning and neuralnetworks)
be able to implement, apply and systematicallyevaluate machine learning techniques
understand probability theory and how it can beused for representing and reasoning with uncertain
knowledge be able to apply basic probability theory to
machine learning problems
understand some probabilistic approaches to text
mining and information retrieval
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Resources
Highly recommended reading Russell S. and Norvig P., Artificial Intelligence:
A modern approach, 2nd edition, 2003.
Used extensively (see reading list)
Available for purchase from the University
Bookshop
Several copies are also available at the library
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Resources (cont)
Handouts
At www.itee.uq.edu.au/~comp3702 you will find
Slides used in lectures
Tutorials
Assignments Readings and handouts
Links and resources
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Times and Venues
Sign-up for Tutorials via SI-net
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Mid-semesterexam(optional)
Assignment 1preparation
Mid-semester exam;Discussion of assignment 1,related theory (6pp, 2pp)
1125 August6
10
Adversarialsearch(Comments,hints)
Applications of AIi- Bioinformatics(Shoaib)
ii- TBA - External Speaker
918 August5
8
InformedSearch(Comments,hints)
Chapter 6Adversarial search, gameplaying (6pp, 2pp)
7
11 August4
6
ProblemRepresentatio
n (Comments,hints)
Chapter 4(except
4.4 and4.5)
Informed search and
exploration (6pp, 2pp)
5
4 August3
4
Assignment 1available
The definitionof artificialintelligence
Chapter 3Solving problems bysearching (6pp, 2pp)
328 July2
2No tutorial
Chapters1, 2 and26: pp.
947-949,958-960.
Introduction to artificialintelligence, an agent-based
perspective (6pp, 2pp)
1
21 July1
Shoaib SehgalPart 1
AssessmentTutorialSession
TBA
ReadingRussell andNorvig, 2003
LectureTues 2-4pm
LectureNumber
MondaysDate
Week Number
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Neural networks(Comments)
Assignment 2preparation
Chapter
23
Robotics and discussion ofassignment 2 (6pp,2pp,(corrected
30/10/07) more onapplications of NN)
2313
Oct
ober
12
22
Decision Trees and NaveBayes Classification(Comments, decision tree
solution)
Chapter 20(20.5-onwards)
Learning algorithms for
neural networks (6pp,2pp, (corrected30/10/07) more on NNlearning)
21
6October
11
Mid-semester break (one week)29 Sep.
20
Current best learning and
decision trees(Comments)
Chapter 20(20.5-
onwards)
Neural networks (6pp, 2pp(corrected 31/10/07),
more material on NNs,NetTalk audio (8MB))
19
22 Sep.10
18
Machine learningbasics (Comments)
Chapter 20(20.1-20.2)
Statistical machinelearning (6pp, 2pp)
1715 Sep.9
16
Assignment 2available
Probabilisticreasoning (Comments)
Chapters18and
19
Principles of machinelearning, decisiontrees (6pp, 2pp,
examples withdecision tree learning)
15
8 Sep.8
14
Assignment 1
deadline(Monday 1stSep., 5pm)
Chapter 13+ 7.1-7.2
Probabilistic reasoning(6pp, 2pp, example)
13
1 Sep.7
Ruth SchulzPart 2
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10 NovemberExam Week 2
Final Exam
3 NovemberExam Week 1
Revision Period
27 October
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Assessment
Assignments (30%)
2 assignments Assignment 1 (10%): Search game playing
Assignment 2 (20%): Machine learning pattern recognition
Tutorials (10%)
Active participation mark (1% per tutorial) Final Examination (60%)
During final examination period
Covers lecture material
Details 2 hours
Closed-book
Primarily short answer / short essay
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Important Assessment Information
All assessment is due at 5pm of the due date
Assignments need to be submitted on-line at
http://submit.itee.uq.edu.au
Late submission not accepted except for medical or
strong personal reasons (documentation required)
The programming language will be Java
Tutorials for C/C++ programmers are available at
the course website.
Why Java ?
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Why Java http://shootout.alioth.debian.org/
Intel AMD
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Assignments
Two assignments Two problems/applications which
require intelligence (artificial or natural)
Problem-solving/optimisation
Approach: clever search algorithms
optimising outcomes on basis of a well-defined current state
exposes computational complexity issues
Pattern recognition Approach: learning-by-example/machine learning
exposes difficulties of representation by rules
illustrates the use of probabilistic methods and neural networks
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Tutorials
Aims
to provide examples of potential examination
questions
to enable and encourage peer-tutoring
to provide an opportunity for questions
to explore the theoretical concepts
to apply the theoretical concepts
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Lectures
Aims
to outline theories, methods and applications of
the field of AI
to explain difficult concepts from the
recommended text and other sources
to illustrate concepts in AI with diagrams and
examples
to provide a forum for general discussion andquestions about the subject matter
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Some tips
Dont be shy, participate in lectures, askquestions.
Buy the book, read chapters as noted. It iswell-written, up-to-date, and an excellent
reference for later. AI is actually quite fun and useful, but you
need to work hard. Assignments and tutorials
will help you to work.
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Examination
Closed book, non-programmable calculator allowed
Knowledge questions - theory may have been in tutorials
explicit in the recommended text book
assessed on correctness of answer
Knowledge questions - practical similar to those in tutorials
method described in the recommended text and lectures
assessed on correctness of method application
Discussion questions may have been addressed in lectures
not necessarily explicit in the readings
assessed on insight / justifications
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Artificial intelligence:
An introduction
after which you should
know a couple of definitions if intelligence and AI
know how the Turing Test works
know the Physical Symbol System Hypothesis
understand the point made in Searles Chinese Room
Argument
have some familiarity with the history of AI
understand the concept of agents in the context of AI
be familiar with some intelligent agent designs
and feel prepared to discuss actual ways of makingmachines clever and making them learn bythemselves
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What is Intelligence?
For each of the following, give three reasons
why:
(a) A dog is more intelligent than a worm.
(b) A human is more intelligent than a dog.
(c) An organisation is more intelligent than an
individual.
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What is Intelligence?(adapted from J. McCarthy Stanford University)
What is intelligence?
One Answer
Intelligence is the computational part of the ability to
achieve goals in the world. Varying kinds and degrees
of intelligence occur in people, many animals and
some machines.
Do you agree with this definition?
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What is AI?(adapted from J. McCarthy Stanford University)
What is artificial intelligence?
One possible answer
It is the science and engineering of making intelligent
machines, especially intelligent computer programs. It
is related to the similar task of using computers to
understand human intelligence AI does not have to confine itself to methods that are
biologically observable.
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What is AI?(adapted from J. McCarthy Stanford University)
Is there a solid definition of intelligence that doesn't
depend on relating it to human intelligence?
Answer.
Not yet. The problem is that we cannot yet
characterise in general what kinds of computational
procedures we want to call intelligent. We understandsome of the mechanisms of intelligence and not
others.
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Definitions of Artificial Intelligence
Computational Intelligence is the study of
the design of intelligent agents [Poole et al:
1998]
AI is concerned with intelligent
behaviour in artefacts [Nilsson: 1998]
The act of creating machines that
perform functions that require intelligence
when performed by people [Kurzweil:
1990]
The study of how to make computers do
things at which, at the moment, people are
better [Rich and Knight, 1991]
Systems that act rationallySystems that act like humans
The study of mental faculties through theuse of computational models [Charniak &
McDermott: 1985]
The study of the computations that make it
possible to perceive, reason, and act
[Winston: 1992]
The exciting new effort to makecomputers think machines with minds,
in the full and literal sense [Haugeland:
1985]
[The automation of] activities that we
associate with human thinking, activitiessuch as decision-making, problem solving,
learning [Bellman, 1978]
Systems that think rationallySystems that think like humans
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Science of AI
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The Turing Test Turing proposed a definition of Artificial
Intelligence independent of the innermechanisms of the machine
http://www.abelard.org/turpap/turpap.htm
Operational test of intelligent behaviour
Given an interrogator, and
(A) a computer and (B) a person in adifferent room
The interrogator, through asking questions,
must determine whether respondent A orrespondent B is the computer. If thecomputer fools the interrogator then we
can call it intelligent.
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The Turing Test
What is
needed?
Knowledge
Reasoning
Language
Learning
http://chatbotgame.com/
Chatbot: Hello there human.
You: are you bot
Chatbot: Of course. I'm a collection of software rules.You: how are you
Chatbot: Life is tolerable. How are things in your world?
You: excellent
Chatbot: Can we change the subject please? Do you like
swimming?You: nop
Chatbot: "nop" ... What does that mean? It's not in my
dictionary.
You: nop = no
Chatbot: That's too bad.You: why
Chatbot: Because of an intense metaphysical connection in
the properties of submolecular particles
You: what?Chatbot: Do you understand me?
You: noChatbot: Well I don't understand humans.. Lol
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Attributes of the Turing Test
Positive
standard test
bypasses the true nature of intelligence
removes bias
Negative
focus on symbolic tasks
compares machine with human intelligence far too restrictive
difficult to do in practice
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Lets play: Spot the Intelligence!
Order the following from most to least
intelligent
Cockroach
Computer
Elevator
Hammer
Human
Model-T ford motor car
Sunflower
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Answers?
Simple yes / no answers
cannot be made from
current definitions.
Your answer reflects
your beliefs, and is not
necessarily correct or
incorrect.
My Ranking Human
Cockroach
Sunflower
Computer
Elevator
Model-T Ford
Motor Car
Hammer
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Examples
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Average Rank (from 2000)
Average Ranking
0
1
2
34
5
6
7
8
Cockroac
h
Compute
r
Elevato
r
Hamme
r
Human
Model-
T
Sunflowe
r
Average
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Results of Intelligence Ranking
Most Popular Response
0
1
2
34
5
6
7
8
Human
Cock
roach
Sunflowe
r
Compute
r
Elevato
r
Model-
T
Hamme
r
Ra
nk
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Physical Symbol System Hypothesis(Simon and Newell, 1976)
A physical symbol system has the necessary and sufficient means for intelligentaction.
A system:
Consists of a set of entities, called symbols,
Symbols can occur as components of another type of
entity called an expression (or symbol structure) A symbol structure is composed of a number of instances
(or tokens) of symbols related in some physical way (suchas one token being next to another).
Contains a collection of these symbol structures
Contains a collection of processes that operate on expressionsto produce other expressions: processes of creation,modification, reproduction and destruction
Set of finite symbols that can be composed to form a potential
infinite set of expressions
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Physical Symbol System Hypothesis(Simon and Newell, 1976)
A physical symbol system has the necessary and sufficient means forintelligent action.
Intelligent human thought: the symbols are encodedin our brains. The expressions are thoughts. Theprocesses are the mental operations of thinking.
A running artificial intelligence program: Thesymbols are data. The expressions are more data. The
processes are programs that manipulate the data.
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Searles Chinese Room Thought
Experiment
You are: Monolingual English speaker locked in a room
You are given a large batch of Chinese writing
a second batch of Chinese script as an output
a set of rules in English for correlating the second batch with thefirst batch
The rules correlate one set of formal symbols with another set offormal symbols
Your responses are indistinguishable from those of Chinese
speakers
Just by looking at your answers, nobody can tell you "don'tspeak a word of Chinese."
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Searles Chinese Room
The Point: No matter
how intelligent a computer seems to behaves and
no matter what programming makes it behave that
way
since the symbols it processes are meaningless
(lack semantics) to it
its not really intelligent
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Searles Chinese Room
Demonstrates that a system can be merely following
rules, but not understanding anything at all.
The system he describes will pass the Turing Test
Behaviour cannot determine extent of understanding
Is following rules sufficient for intelligence?
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Strong AI vsWeak AI
Strong AI
duplication of intelligence
aims to understand intelligence
Weak AI
simulation of intelligence
aims to make computers more useful
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Modern History
1956
John McCarthy coined the term artificial intelligence as
the topic of the Dartmouth Conference.
Demonstration of AI program - the Logic Theorist (LT) -
written by Newell, Shaw and Simon (CMU).
1952-62
Samuel (IBM) wrote the first game-playing program that
learns.
1962 First industrial robot company, Unimation, is founded.
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Modern History
1965
Weizenbaum (MIT) built ELIZA, an interactive programthat carries on a dialogue in English on any topic. Itbecame a popular toy at AI centres.
See http://www-ai.ijs.si/eliza-cgi-bin/eliza_script
Or http://www.manifestation.com/neurotoys/eliza.php3
1967 Dendral program (Feigenbaum, Lederberg, Buchanan and
Sutherland) is the first successful knowledge-basedprogram for scientific reasoning.
1968 Minsky and Papert publish Perceptrons, demonstrating
the limits of simple neural nets.
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Modern History
1969
SRI robot, Shakey, demonstrates combining
locomotion, perception and problem solving.
1974
The first expert system - MYCIN (Stanford) -
demonstrates the power of rule-based systems for
knowledge representation and inference in the
domain of medical diagnosis and therapy.
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Modern History
1969-1979
Knowledge-based (expert) systems
1980-1988
Expert systems industry booms
1988-1993
Expert systems industry busts AI winter1966 US Military report on machine translation: The spirit is willing but the
flesh is weak was re-translated (English-Russian-English) intothe vodka is good but the meat is rotten. Prospects seemed poor and
funding was cut.
2008 version (Babel fish): Spirit is willingly ready but flesh it is weak
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Modern History
Mid-1980s-present The return of Neural Networks
1985 Brooks (MIT) develops the concept of behaviour based robots
1988-present Resurgence of probability: general increase in technical depth
Computational Intelligence / soft computing (Evolutionarycomputing, Swarm Intelligence, Fuzzy systems, NNs)
1995-present Agents, agents everywhere
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Agents
Generally computers are obedient, literal,
unimaginative servants
acceptable for most applications
However, increasingly we require systems
that can decide for themselves
Such computer systems are known as agents
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I, Robot
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Agents and Environments
Environment Agent?
SensorsPercepts
ActionsThe agent takessensory input from the
environment and
produces as outputsactions that affect it.
Agents include humans,robots, softbots,thermostats, etc
Effectors
Rationality depends on
performance, degree of success,perceptual history,knowledge of environment, andactions available for deployment
Lets look at these aspects in terms
of different agent designs
An agent consists of
an architecture, anda program.
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Agent Types
Simple reflex agents
Model-based reflex agents Goal-based agents
Utility-based agents
Example: Automated taxi driver
Cameras,
speedometer,
GPS, sonar,
microphone
Steer,
accelerate,
brake, talk to
passenger
Roads, other
traffic,
pedestrians,
customers
Safe, fast, legal,
comfortable trip,
maximise profits
Taxi Driver
SensorsActuatorsEnvironmentPerformance
MeasureAgent Type
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Simple reflex agents
En
vironment
Agent
Condition-action rulesWhat action I
should do now
What the worldis like now
Actuators
Sensors
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Model-based reflex agents
En
vironment
Agent
Condition-action rulesWhat action I
Should do now
What the world islike now
Actuators
Sensors
What my actions do
How the world evolves
State
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Goal-based agents
En
vironment
Agent
GoalsWhat action I
should do now
What the world islike now
Actuators
Sensors
What my actions do
How the world evolves
State
What it will be likeif I do action A
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Utility-based agents
En
vironment
Agent
UtilityHow happy I will be
in such a state
Actuators
Sensors
What my actions do
How the world evolves
State
What it will be likeif I do action A
What action Ishould do now
What the world islike now
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Questions ?
Office HoursTuesday 10-12 AM
Room 308, Axon building ITEE
Acknowledgements:http://domus.usherbrooke.ca
www.ironbot.com
for some of the images used in making of these slides.