Download What is Artificial Intelligence

HIT3002: Introduction to
Artificial Intelligence
Subject Overview
Staff information
„
Bao Vo (Lecturer, Convenor)
… Email: [email protected]
… Phone: (9214) 4756
… Office: EN504
„
Li Minyi (Tutor)
… Email: [email protected]
Swinburne University of Technology
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Subject Roadmap
„
Introduction
„
Intelligent agents
„
Search
„
Knowledge representation and reasoning
„
Planning
„
Probabilistic reasoning and Bayesian networks
„
Adaptation and learning
Meeting time
„
Lectures:
… When: Monday 11.30 – 13.30
… Where: EN203
„
Tutorials:
… Starting Week 2 (March 7th)
… Group 1: Monday 13.30 – 14.30 (Room: EN406)
… Group 2: Monday 9.30 – 10.30 (Room: EN202)
„
Consultations:
… When: Monday 15.30 – 17.00
… Where: EN504
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Subject Assessment (Provisional)
„
2 Assignments – 20% and 20%
… Progress on assignment must be shown in tutorials
… Involves problem solving
„
Exam – 60%
… Focus will be on conceptual understanding of all main topics
covered in the lectures and the work of the tutorials
… Tutorials will help in facing the exam with confidence!
„
Exam is
E
i a hhurdle!
dl ! I.e.,
I you mustt gett att lleastt 50% on the
th final
fi l
exam to pass the subject.
Subject Information
„
Textbook
… Russell, S.J., Norvig, P.,
Artificial Intelligence: A Modern Approach,
3rd edition, Prentice-Hall, 2010. [AIMA]
„
References
… B. Coppin “Artificial Intelligence Illuminated” Jones and Bartlett Publishers, 2004
… Nilsson “Artificial Intelligence: A New Synthesis” Morgan Kaufman Pub. 1998
„
Core Aims
… Understand fundamental concepts of Artificial Intelligence (AI) and generic
problem solving techniques
… Apply advanced algorithms and data structures to solve common problems
… Design simple software that implements AI concepts.
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Lectures
„
Focus is on theory
„
High-level conceptual discussion of the algorithms
„
Assignment discussion (10-15 minutes)
„
Q & A of topics covered so far (10-15 minutes)
Tutorials
„
Aim is to ensure that all examinable aspects (theory) are
fully covered in these sessions
… Discussion on all fundamental topics in the subject
… Some simple problems that are to be solved using AI techniques
„
Assignment progress monitoring
„
Discussion on potential exam questions, sample exam
p p
paper
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HIT3002: Introduction to
Artificial Intelligence
Introduction
Why Artificial Intelligence (AI)
„
It’s not just a good topic for science fiction
„
There are successful stories of using AI:
… Expert systems: makes 100’s of millions of dollars per year (for
the mining and banking industries)
… AI techniques in search engines (specifically targeted by Google)
… AI techniques in automotive industry: BMW’s iDrive, Holden’s AI
stabilisation system
… Fuzzy-logic controllers can be found in washing machines, dish
washers, camera autofocus, air conditioners, car automatic
transmissions, etc.
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Why Artificial Intelligence (AI)
„
Last year HIT3002 students’ success stories:
… A student received the Faculty Summer ’08 Vacation Scholarship
to work on a pproject
j at Thales Groupp ((Australia))
… Using AI techniques to develop solutions for Air Traffic Control
Management (ATM) systems
… Offered an internship by Thales to continue to work on the project while
studying full-time in his last year at Swinburne
… Another student received the Faculty Summer ’08 Vacation
Scholarship to work on a project at Swinburne developing nextgeneration
ti social
i l networks
t k
… Another student received the Faculty Summer ’08 Vacation
Scholarship to work on a project for cloud computing
Why study AI
„
It provides the core knowledge of computer science
„
You’ll learn to analyse problems and learn about
t h i
techniques/algorithms
/ l ith tto solve
l real-world
l
ld problems
bl
„
It paves the way to understanding various sorts of
intelligence (in both humans and machines)
„
It is also fun (and different to most other subjects)
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What is intelligence
„
Faculty of understanding
„
Capacity to know or apprehend
„
Available ability as measured by intelligence tests or social criteria
„
Ability to use knowledge in new situations or problems
„
Ability to learn
„
Ability to plan, to foresee problems
„
Ability to use symbols and relationships
„
Abilit to think abstractly,
Ability
abstractl to work
ork towards
to ards goals
„
Ability to act/perform certain functions
„
… you name it
What is Artificial Intelligence
„ Different definitions due
to different criteria
… Two dimensions:
…Thought processes/reasoning vs. behavior/action
…Success according to human standards vs. success according
to an ideal concept of intelligence: rationality.
Systems
y
that think like humans Systems
y
that think rationallyy
Systems that act like humans
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Systems that act rationally
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Systems that act like humans
„
AI is the art of creating machines that perform
functions that require intelligence when performed
by humans
„
Methodology: Take an intellectual task at which
people are better and make a computer do it
„
Turingg test
•Prove a theorem
•Play chess
•Plan a surgical operation
•Diagnose a disease
•Navigate in a building
Systems that act like humans
„
When does a system behave intelligently?
… Turing (1950) Computing Machinery and Intelligence
… Operational
O
ti l ttestt off iintelligence:
t lli
iimitation
it ti game
… Test still relevant now, yet might be the wrong question.
… Requires the collaboration of major components of AI: knowledge,
reasoning, language understanding, learning, …
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Systems that think like humans
„
How do humans think?
… Requires scientific theories of internal brain activities (cognitive
model):
… Level of abstraction? (knowledge or circuitry?)
… Validation?
… Predicting and testing human behavior
… Identification from neurological data
… Cognitive Science vs. Cognitive neuroscience.
„
Both approaches are now distinct from AI
„
Share that the available theories do not explain anything
resembling human intelligence.
… Three fields share a principal direction.
Systems that think like humans
„
Some references;
… Daniel C. Dennet.
Consciousness explained.
… M. Posner (edt.)
Foundations of cognitive
science
… Francisco J. Varela et al.
The Embodied Mind
… J.-P. Dupuy. The
mechanization of the mind
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Systems that think rationally
„
Capturing the laws of thought
… Aristotle: What are ‘correct’ argument and thought
processes?
…Correctness depends on irrefutability of reasoning processes.
… This study initiated the field of logic.
…The logicist tradition in AI hopes to create intelligent systems
using logic programming.
… Problems:
P bl
…Not all intelligence is mediated by logic behavior
…What is the purpose of thinking? What thought should one have?
Systems that think rationally
„
A reference;
… Ivan Bratko, Prolog
programming for artificial
intelligence.
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Systems that act rationally
„ Rational
behavior: “doing the right thing”
…The “Right
Right thing”
thing is the course of action that is
expected to maximize goal achievement given the
available information.
„ Can include thinking, yet
action.
in service of rational
…Action without thinking: e.g. reflexes.
Systems that act rationally
„
Two advantages over previous approaches:
… More
M generall than
th law
l off thoughts
th ht approachh
… More amenable to scientific development.
„
Yet rationality is only applicable in ideal environments.
„
Moreover rationality is not a very good model of reality.
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Systems that act rationally
„
A reference:
… Rolf Pfeifer and Christian
Scheier. Understanding
Intelligence. MIT Press,
1999.
Foundations of AI
„
Different fields have contributed to AI in the form of
ideas,viewpoints and techniques.
… Philosophy: Logic,
Logic reasoning
reasoning, mind as a physical system
system, foundations
of learning, language and rationality.
… Mathematics: Formal representation and proof algorithms,
computation, (un)decidability, (in)tractability, probability.
… Psychology: adaptation, phenomena of perception and motor control.
… Economics: formal theory of rational decisions, game theory.
… Linguistics: knowledge represetatio, grammar.
… Neuroscience: physical substrate for mental activities.
… Control theory: homeostatic systems, stability, optimal agent design.
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A brief history
„
What happened after WWII?
… 1943:
1943 Warren
W
Mc
M Culloch
C ll h andd W
Walter
lt Pitt
Pitts: a model
d l off
artificial boolean neurons to perform computations.
…First steps toward connectionist computation and learning
(Hebbian learning).
…Marvin Minsky and Dann Edmonds (1951) constructed the first
neural network computer
… 1950: Alan Turing’s “Computing Machinery and Intelligence”
…First complete vision of AI.
A brief history (2)
„
The birth of AI (1956)
… Darmouth Workshop bringing together top minds on automata theory, neural
nets and the study of intelligence.
… Allen Newell and Herbert Simon: The logic theorist (first nonnumerical thinking program
used for theorem proving)
… For the next 20 years the field was dominated by these participants.
… Great expectations (1952-1969)
… Newell and Simon introduced the General Problem Solver.
… Imitation of human problem-solving
problem solving
… Arthur Samuel (1952-)investigated game playing (checkers ) with great success.
… John McCarthy(1958-) :
… Inventor of Lisp (second-oldest high-level language)
… Logic oriented, Advice Taker (separation between knowledge and reasoning)
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A brief history (3)
„
The birth of AI (1956)
… Great expectations continued ..
… Marvin Minskyy ((1958 -))
… Introduction of microworlds that appear to require intelligence to solve: e.g. blocks-world.
… Anti-logic orientation, society of the mind.
„
Collapse in AI research (1966 - 1973)
… Progress was slower than expected.
… Unrealistic predictions.
… Some
S
systems
t
lacked
l k d scalability.
l bilit
… Combinatorial explosion in search.
… Fundamental limitations on techniques and representations.
… Minsky and Papert (1969) Perceptrons.
A brief history (4)
„
AI revival through knowledge-based systems (19691970)
… General-purpose
G
l
vs. ddomain
i specific
ifi
…E.g. the DENDRAL project (Buchanan et al. 1969)
… First successful knowledge intensive system.
… Expert systems
…MYCIN to diagnose blood infections (Feigenbaum et al.)
… Introduction of uncertainty in reasoning.
… Increase in knowledge representation research.
…Logic, frames, semantic nets, …
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A brief history (5)
„
AI becomes an industry (1980 - present)
… R1 at DEC (McDermott, 1982)
… Fifth generation project in Japan (1981)
… American response …
„
Puts an end to the AI winter.
„
Connectionist revival (1986 - present)
… Parallel distributed processing (RumelHart and McClelland,
1986); backprop.
A brief history (6)
„
AI becomes a science (1987 - present)
… Neats vs. scruffies.
…In speech recognition: hidden markov models
…In neural networks
…In uncertain reasoning and expert systems: Bayesian network
formalism
……
„
The emergence of intelligent agents (1995 - present)
… The whole agent problem:
“How does an agent act/behave embedded in real environments
with continuous sensory inputs”
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State of the art
„
Deep Blue defeated the reigning world chess champion Garry
Kasparov (1997)
„
Proved a mathematical conjecture (Robbins conjecture) unsolved for
decades
„
“No hands” across America (driving autonomously 98% of the time
from Pittsburgh to San Diego)
„
During the 1991 Gulf War, US force deployed an AI logistics planning
and scheduling program that involved up to 50,000 vehicles, cargo,
and people
„
NASA’s on-board autonomous planning program controlled the
scheduling of operations for a spacecraft
„
Proverb solves crossword puzzles better than most humans
Summary
„
Different people think of AI differently.
„
Two important questions to ask are: Are you concerned
with
ith thinking
thi ki or behavior?
b h i ? Do
D you wantt to
t model
d l hhumans
or work from an ideal standard?
„
In this course, we adopt the view that intelligence is
concerned mainly with rational action.
„
Ideally,
y, an intelligent
g agent
g takes the best ppossible action
in a situation. We will study the problem of building agents
that are intelligent in this sense.
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