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Advanced Artificial IntelligenceAdvanced Artificial IntelligenceAdvanced Artificial IntelligenceAdvanced Artificial Intelligence
VV ThTh HngHng NhnNhn
([email protected])([email protected])
Faculty of Information TechnologyFaculty of Information Technology
University of Engineering & TechnologyUniversity of Engineering & Technology
VNU, HanoiVNU, Hanoi
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Objectives of this courseObjectives of this courseObjectives of this courseObjectives of this course
To introduce students to the field of AI
To explain the challenges inherent in building an intelligent system
To explain
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e ey para gms, ore tec n ques, gor t ms
Understand the role of basics
Knowledge representation
Learning methods in AI, in engineering intelligent systems
Assess the applicability, strengths, and weakness of these methods in
solving particular engineering problems
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ScheduleScheduleScheduleSchedule
WeekWeek DayDay LectureLecture RemarkRemark
1 Dec. 07 Introduction to AI
2 Dec. 14 Intelligent agents
3 Dec. 21 Knowledge representation & Proposition Logic
4 Dec. 28 First order logic
5 Jan. 4 Rule-based system
6 Jan. 11 Rule based Expert System
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.
8 Jan. 25 Mid-term
9 Feb. 01 Fuzzy reasoning Student seminar
10 Feb. 08 Introduction to learning
11 Feb. 15 Rule induction & Decision tree
12 Feb. 22 Probabilistic learning
13 Feb. 29 Neural network
14 Mar. 01 Natural language processing Student seminar
15 Mar. 08 Final
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Introduction to AIIntroduction to AIIntroduction to AIIntroduction to AI
1.1. What is AIWhat is AI
2.2. Example systemsExample systems
3.3. Approaches to AIApproaches to AI
4.4. A brief historyA brief history
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1. What is AI?1. What is AI?1. What is AI?1. What is AI?
Artificial intelligence
Is concerned with the design of intelligence in an artificial device
Its difficult to define the term AI simply & robustly
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The term AI was coined by John McCarthy, 1956
The goal of AI is to develop machines that behave as though they were
intelligent
What is intelligence?
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1. What is AI? (cont.)1. What is AI? (cont.)1. What is AI? (cont.)1. What is AI? (cont.)
What is intelligence? Humans?Humans?
If we take human beings to be intelligent,
AI is something that is characterized as humans or something that has behavior
like humans
Two school of thou hts
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Systems/ Machines behave intelligently as a human
Humans dont believe intelligently all the time, AI concerns machines that behave
rationally
Two main types of behaviorsbehaviors
Thinking intelligently: reasoning intelligently and properly in order to come up
with a solution
Act/ behave intelligently
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1. What is AI? (cont.)1. What is AI? (cont.)1. What is AI? (cont.)1. What is AI? (cont.)
Look at different ways of defining AI
Thought processes/reasoning vs. behavior
How to measure performance
Human-like performance vs. ideal performance
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Behavior
Ideal
performance
(rationally)
Human-like
performance
A diagram that shows the 4 different definitions that emerge
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Approach to AIApproach to AIApproach to AIApproach to AI
Thought/reasoning
Ideal
Systems that think like
humans
(Alan Turing testAlan Turing test)
Systems that think
rationally
(Laws of thought/LogicLaws of thought/Logic)
1. What is AI?1. What is AI?1. What is AI?1. What is AI?
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Behavior
performance(rationally)
-performance
Systems that act
rationally
(Rational agentRational agent)
Systems that act like
human
(Cognitive scienceCognitive science )
A diagram that shows the 4 different definitions that emerge
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Turing TestTuring TestTuring TestTuring Test
1. What is AI?1. What is AI?1. What is AI?1. What is AI?
The interrogator asks questions
The being inside the roombeing inside the room
processes the questionsprocesses the questions &
return answers
a computer
human
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The interrogator receives the
answers on a screenanswers on a screen
He need to make out from the
answer whether the beingwhether the being
inside the room is computerinside the room is computer
or humanor human
an interrogator outside the
room doesnt know the
being inside the room is either
computer or human
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Turing Test (cont.)Turing Test (cont.)Turing Test (cont.)Turing Test (cont.)
The computer tries to convinces that it is human
The interrogator must decide who is human
If the interrogator cannot reliably distinguishcannot reliably distinguish the human from the
1. What is AI?1. What is AI?1. What is AI?1. What is AI?
computer
Then the computercomputer does posses (artificial) intelligence
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Typical AI problemTypical AI problemTypical AI problemTypical AI problem
1. What is AI?1. What is AI?1. What is AI?1. What is AI?
Intelligent entities (agent) need to be able to do both
Mundane & expert tasks
Mundane tasks
PlanningPlanning route, activity
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RecognizingRecognizing people, objects (through vision)
CommunicatingCommunicating (through natural language)
NavigatingNavigating round obstacles on the street
Expert tasks Medical diagnosis
Mathematical problem solving
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Typical AI problem (cont.)Typical AI problem (cont.)Typical AI problem (cont.)Typical AI problem (cont.)
1. What is AI?1. What is AI?1. What is AI?1. What is AI?
Which of these problems are easy/hard?
Surprisingly, it has been
easier to mechanize many of the high-level tasks which are so-calledex ert tasks in the histor of AI
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easier to solve the problem in the domain of expert
E.g.,
symbolic integration
Proving theorems
Playing chess
Medical diagnose
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Typical AI problem (cont.)Typical AI problem (cont.)Typical AI problem (cont.)Typical AI problem (cont.)
1. What is AI?1. What is AI?1. What is AI?1. What is AI?
AI doesnt have the same success in dealing with
mundane task
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E.g., walking around without running into things
Catching prey and avoiding predators
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Intelligent behaviorIntelligent behaviorIntelligent behaviorIntelligent behavior
Perception
Reasoning
1. What is AI?1. What is AI?1. What is AI?1. What is AI?
earn ng
Understanding language
Solving problems
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2. Example systems2. Example systems2. Example systems2. Example systems
Computer vision
Image recognition
Robotics
Natural language processing
Speech processing
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Practical impact of AIPractical impact of AIPractical impact of AIPractical impact of AI
AI components are embedded in numerous devices
E.g., copy/vending machines
AI systems are in everyday use
Detectin credit card fraud
2. Example systems2. Example systems2. Example systems2. Example systems
Configuring products
Aiding complex planning tasks
Advising physicians
Intelligent tutoring systems
Provide students with personalized attentions
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2. Example systems (cont.)2. Example systems (cont.)2. Example systems (cont.)2. Example systems (cont.)
Machine translationMachine translation
Immediate translation between people speaking different languages
Would be a remarkable achievement of enormous economic and
cultural benefit
Autonomous agentsAutonomous agents
In space exploration, robotic space probes autonomously monitor their
surroundings, makes decisions & act to achieve their goals
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Internet agentsInternet agentsInternet agentsInternet agents
The explosive growth of the internet has also led to
growing interest in internet agent
2. Example systems2. Example systems2. Example systems2. Example systems
Seek needed information
Learn which information is most useful
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3. Approaches to AI3. Approaches to AI3. Approaches to AI3. Approaches to AI
Strong AIStrong AI aims to build machines
that can truly reason & solve problem which is self-aware
& whose overall intellectual ability is distinguishable from that of a human being
Can be human-like
or non-human-like
When AI was first conceived in the 1950s and 1960s there were a huge
optimism about AI
A prediction that very soon AI systems will be able to overtake humans
Can do anything that humans can & can do much better
Even can do the task that humans cannot within a short time
But we now know the true difficulty that AI faces
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3. Approaches to AI (cont.)3. Approaches to AI (cont.)3. Approaches to AI (cont.)3. Approaches to AI (cont.)
Weak AI:Weak AI: deals with the creation of some form of AI of computer-based
artificial intelligence
they cannot truly reason and solve problems, but can act as if they were
intelligent
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Weak AI holds that
Suitably programmed machines can simulate human recognition
Strong AI really deal with
machines that have mental states that think, reason, understand
behaviors
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3. Approaches to AI (cont.)3. Approaches to AI (cont.)3. Approaches to AI (cont.)3. Approaches to AI (cont.)
Applied AI
Aims to produce commercially viable smart systems
E.g., a security system that is able to recognize the faces of people who
are permitted to enter a particular building
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Applied AI has already enjoyed considerable success
Cognitive AI
Computers are used to test theories about how the human mind works
E.g., theories about how we recognize faces & other objects, or about
how we solve abstract problem
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AI topicsAI topicsAI topicsAI topics
3. Approaches to AI3. Approaches to AI3. Approaches to AI3. Approaches to AI
Core areasCore areas
Knowledge representation
Reasoning
Machine learning
General algorithmsGeneral algorithms
Search
Planning
Constraint satisfaction
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PerceptionPerception
Vision
Natural language
Robotics UncertaintyUncertainty
Probabilistic approaches
ApplicationsApplications
Game playing
AI & education
Distributed agents Decision theoryDecision theory
Reasoning with symbolic dataReasoning with symbolic data
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Limits of AI todayLimits of AI todayLimits of AI todayLimits of AI today
Todays successful AI systems
Operate in well-defined domains
Employ narrow, specialized knowledge
3. Approaches to AI3. Approaches to AI3. Approaches to AI3. Approaches to AI
Commonsense knowledge
Needed in complex, open-ended worlds
Understand unconstrained Natural Language
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4. AI history4. AI history4. AI history4. AI history
The dream of making a computer imitate us began many centuries
ago
Intellectual roots of AI stretch back thousands of years into the
The concept of intelligent machine is found in Greek mythology
8th century
Hephaestus created a huge robot, Talos to guard Crete inland
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FoundationsFoundationsFoundationsFoundations
4. AI history4. AI history4. AI history4. AI history
Psychology
Physiology Biology
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Artificialintelligence
Mathematics
Economics Linguistics
Computerengineering
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The main movements of AIThe main movements of AIThe main movements of AIThe main movements of AI
4. The history of AI4. The history of AI4. The history of AI4. The history of AI
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The first beginningsThe first beginningsThe first beginningsThe first beginnings
In the 1930s, Godel, Church, & Turing
laid important foundations for logic and theoretical computer
science
4. The history of AI4. The history of AI4. The history of AI4. The history of AI
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In the 1940s, based on the results from neuroscience
McCulloch, Pitts, and Hebb designed the first mathematical
models of neural networks
Computers at that time lacked sufficient power to simulate
simple brains
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4. The history of AI4. The history of AI4. The history of AI4. The history of AI
AI as a science of thought mechanization could begin once there
were programmable computers
In the 1950s
Logic solves almost all problemsLogic solves almost all problemsLogic solves almost all problemsLogic solves almost all problems
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,
computers, which actually work with numbers, one can process symbols
McCarthy introduced a programming language with the language LISP,
esp. for the processing of symbolic structures
Both of these systems were introduced in 1956 at the Darthmouth
conference, which is considered the birthday of AI
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4. The history of AI4. The history of AI4. The history of AI4. The history of AI
Logic solves almost all problems (cont.)Logic solves almost all problems (cont.)Logic solves almost all problems (cont.)Logic solves almost all problems (cont.)
In the 1970s, the logic programming language PROLOG was introduced
Offers the advantage of allowing direct programming using Horn clauses, a
subset of predicate logic
Until the 1980s
A breakthrough spirit dominated AI, esp. among logicians, thanks to the string of
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impressive achievements in symbol processing
With the 5th Generation Computer System project in Japan & ESPRIT program in
Europe, heavy investment into the construction of intelligent computers
For small problemsFor small problems, automatic provers & other symbol processingsystems sometimes worked very well
But, the combinatorial explosion of the search space defined a narrow
window for these successes
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4. The history of AI4. The history of AI4. The history of AI4. The history of AI
The new connectionismThe new connectionismThe new connectionismThe new connectionism
Computer scientist, physicians, and cognitive scientists showed that
Mathematically modeled neural networks are capable of learning using training
examples to perform tasks which previously required costly programming
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-
patterns, considerable successes became possible, esp. in pattern recognition
The neural networks could acquire impressive capabilities
Attempts to combine neural networks with logical rules or the knowledge of
human experts met with great difficulties
No satisfactory to the structuring & modularization of the network was found
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4. The history of AI4. The history of AI4. The history of AI4. The history of AI
Reasoning under uncertaintyReasoning under uncertaintyReasoning under uncertaintyReasoning under uncertainty
One of wishes to unite logics ability to explicitly represent knowledge with
neural networks strength in handling uncertainty
Several alternatives
The most promising, probabilistic reasoning, works with conditional probabilities
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for propositional calculus formulas
Since then, many diagnostic & expert systems have been built for problems of
everyday reasoning using Baysian Networks
Since 1990, data mining has developed
as a subdiscipline of AI in the area of statistical data analysis for extraction of
knowledge from large databases
Bring no new techniques to AI, rather it introduces the requirement of using large
DB to gain explicit knowledge
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SummarySummarySummarySummary
Different definitions of AI
Thought/reasoning vs. behavior
Human-like performance vs. ideal performance (rationally)
Example systems
Approaches to solving AI problems
Strong AI, weak AI, applied AI, cognitive AI
Brief history
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QuestionsQuestionsQuestionsQuestions
1. Define intelligence
2. What are the different approaches in defining AI?
3. Suppose you design a machine to pass the Turing Test. What are the
capabilities such a machine must have?
4. Will building an artificially intelligent computer automatically shed light on
the nature of natural intelligence
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