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
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1 What are AI and Intelligent Systems?
Systems that can do something intelligently
ThinkingActing
Humans
Rationality
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AI Systems:
Think like humans
Think rationally
Real World:
Act like humans
Act rationally
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• Acting Humanly
AI systems (intelligent systems):
- Understanding natural language
- Reasoning
- Learning
- Vision
- Motion
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But, what is the fundamental issue?
System
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Turing Test
Screen 1 Screen 2Room 1 Room 2
womanprogram
interrogator
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• 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!
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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!
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• Thinking humanly
Program
thinks like a human
How do humans think?
Cognitive science: Computer Science (AI) and Psychology
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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”.
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• Acting rationally
Beliefs Goal
inference?
Beliefs Goal
inference + rationality
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2 Foundations of AI
AI
Computer
Psychology (1879 )
Engineering (1940 )
Mathematics (C. 800 )
Philosophy (428 B.C. )
(1957 )Linguistics
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• 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
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Mind PhysicalDevice
Resoning+
KnowledgeSet of Laws
?
Knowledge Acquisition
EmpiricismInductionObservation· · ·
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Connection between knowledge and action
Knowledge(beliefs) Goal
action?
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• Mathematics
AI Programs
Logic ProbabilityComputation
Computation
Formal AlgorithmsDecidabilityIntractabilityNP - Complete
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Logic
RepresentationReasoning
Probability
UncertaintyPossibility · · ·
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• Psychology
Behaviourism
study mental constructs(knowledge, beliefs, goals, · · ·)
by using experiment approaches(stimulus - response)
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Cognitive Psychology
How brain possesses and processes information?
Stimulus - Response:
stimulus =! internal representationrepresentation =! new internal representation
cognitive process:
new internal representation =! action (response)
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• 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.
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• Linguistics
Language learning
Language understanding
Formal description of language
+ AI
or called
Computationallinguistics
nature languageprocessing
Modern linguistics
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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
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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
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• 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
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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
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• 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
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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
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• 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· · ·
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• 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
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4 Overview of AI and Intelligent SystemsAI
AI Foundations AI Systems
Search KnowledgeRepresentation
Learning RoboticsPlanning
... ...
Systems ProcessingNatural LanguageKnowledge Based
...
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• 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
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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!
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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!
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• Search
Start
Goal2
2
1.5
3
3 3.2
2
3
2.8
2
Find the shortest path from start to goal
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• 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
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• AI Systems
IntelligentAction
Planning
Learning
KBS...
Planning System: Generate actions to reach a goal:
Actions
Goal
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Learning System (knowledge discovery)
LearningObservations
ConceptualKnowledge
KBS: Expert Systems, Intelligent Systems
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5 Tutorial and Lab Questions
(1) Question 1.14 on page 32.
(2) Question 2.9 on page 63 (this is a programming task).