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Introduction to Computer Science
Polly Huang
NTU EEhttp://cc.ee.ntu.edu.tw/~phuang
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Chapter 10
Artificial Intelligence
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Chapter 10: Artificial Intelligence
10.1 Intelligence and Machines
10.2 Understanding Images
10.3 Reasoning
10.4 Artificial Neural Networks
10.5 Genetic Algorithms
10.6 Other Areas of Research
10.7 Considering the Consequences
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Computer vs. Human
Machine
Performs precisely defined tasks with speed andaccuracy
Not gifted with common sense Human
Capable of understanding and reasoning
More likely to understand the results and
determine what to do next
Not gifted with complex computations
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Humanlike Computer
The ideal hybrid
Continue without human intervention when
faced with unforeseen situations
Possesses or simulate the ability to reason
Psychologists and their models may behelpful
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Intelligent Agents
Agent
Device that responds to stimuli from its
environment
Sensors: to receive stimuli Actuators: to react
The goal of artificial intelligence
To build agents that behave intelligently
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Related Fields
PhilosophyArtificial
Intell igence
ComputerScience
Linguistics
Psychology
BiologyMathematics
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AI Research Approaches
Performance oriented Researcher tries to maximize the performance of
the agents
Just do it Exhaustive search, probabilistic deduction
Computer scientists approach
Simulation oriented Researcher tries to understand how the agents
produce responses.
Wait, let me figure whats going on first Heuristic search, classification
Psychologists approach
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The Eight Puzzle Problem
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Puzzle-Solving Machine
Sensor
Actuator
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Issues Involved
Sensor Camera
Understanding the images (10.2)
Finding a solution (10.3)
Actuator
Based on the solution
Move arms to slide the tiles (Robotics 10.6)
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Levels of Intelligence:Not Really Intelligent
Weak AI
1. Reflex
Actions are fixed and predetermined
2. Context aware Actions affected by knowledge of the
environment
Context information
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Levels of Intelligence:
Trying to be Really Intelligent
Strong AI
3. Goal seeking
Search for a solution
Key: efficient searching
4. Learning
Deduce from experience
Key: identifying majority
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Turing Test
Proposed by Alan Turing in 1950
Benchmark for progress in artificialintelligence
Human interrogator communicates with
test subject by typewriter
Can the human interrogator distinguish
whether the test subject is human ormachine?
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Chapter 10: Artificial Intelligence
10.1 Intelligence and Machines
10.2 Understanding Images
10.3 Reasoning
10.4 Artificial Neural Networks
10.5 Genetic Algorithms
10.6 Other Areas of Research
10.7 Considering the Consequences
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Understanding ImagesComputer Vision
Template matching Compare two bitmaps
Ex. recognizing well-formed characters
Image processing Consider characters by the common shape
Ex. recognizing hand-written characters Edge enhancement
Region finding
Smoothing
Image analysis Guess what partial, obstructed objects are
Ex. recognizing what the image means
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Production Systems Capturing common characteristics of
reasoning problems
1. Collection of states Start or initial state
Goal state
2. Collection of productions Rules or moves Each production may have preconditions
3. Control system Production to apply next
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Applications
Playing games
8 Puzzles, chess
Drawing logical conclusions from givenfacts
Reasoning
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Ex. 8 Puzzle
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Ex. Deductive Reasoning
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Control System Search tree
Record of state transitions explored whilesearching for a goal state
Searching for goal Searches the state graph to find a path from the
start node to the goal
Strategies
Root: start state Children: states reachable by applying one
production
Walking up the tree from the goal
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An Unsolved Eight Puzzle
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Breadth-First Search Tree
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Production Stack
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Quiz Time!
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Types of Searches
Blind
Breadth-first search
Depth-first search
Heuristics
Proximity to goal
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Heuristic Search
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Good Heuristics
Easier to compute than a completesolution
Provide a reasonable estimate of
proximity to a goal
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Heuristic Search Algorithm
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Heuristic Search: Beginning
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Heuristic Search: 2 passes
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Heuristic Search: 3 Passes
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Heuristic Search: Completion
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Quiz Time!
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Chapter 10: Artificial Intelligence
10.1 Intelligence and Machines
10.2 Understanding Images
10.3 Reasoning
10.4 Artificial Neural Networks
10.5 Genetic Algorithms
10.6 Other Areas of Research
10.7 Considering the Consequences
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Neural Networks
Artificial Neuron
Input multiplied by a weighting factor
Output
1 if sum of inputs exceeds a threshold value
0 if otherwise.
Network is programmed by adjustingweights using feedback from examples.
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A Biological Neuron
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Neuron as Processing Unit
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An Example
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One Network, Two Programs
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Uppercase C and T
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Various Orientations
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Character Recognition
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1st Level Processing Units
One processing unit per 9 cells Center cell weight = 2
Other cell weight = -1
Input value per cell 1, if highlighted
0, otherwise
Threshold 0.5
Only when center square is highlighted andone or less other cells also highlighted, theoutput will be 1
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2nd Level Processing Units All outputs from the 1st level
Weight 1
Threshold 0.5
The final output will be 1 (character T) when
at least one output from the 1st
levelprocessing unit is 1
The final output will be 0 (character C) whenno output from the 1st level processing unit is1
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The Letter C
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The Letter T
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Associative Memory
Associative memory
The retrieval of information relevant to the
information at hand
Application of neural networkGiven a partial pattern
Transition themselves to a completedpattern.
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Example Neural Network1. Circle
Processing unit
2. Number in circleThreshold
3. Line Output tobe the input of
the connectedprocessing unit
4. Number on lineWeight of input
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Stablization
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Crossing Two Strategies
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ApplicationConfiguring Neural Networks
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Chapter 10: Artificial Intelligence
10.1 Intelligence and Machines
10.2 Understanding Images
10.3 Reasoning
10.4 Artificial Neural Networks
10.5 Genetic Algorithms
10.6 Other Areas of Research
10.7 Considering the Consequences
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Language Processing
Syntactic analysis
Semantic analysis
Contextual analysis
Information retrieval
Information extraction (knowledgerepresentation)Semantic net
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A Semantic Net
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Robotics
Before
A field within mechanical and electrical
engineering
NowA much wider range of activities
Robocup competition
Evolutionary robotics
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Expert systems
Software package to assist humans insituations where expert knowledge isrequired
Example: medical diagnosis
Often similar to a production system
Blackboard modelSeveral problem-solving systems share a
common data area
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Debates
When should a computers decision betrusted over a humans?
If a computer can do a job better than ahuman, when should a human do the jobanyway?
What would be the social impact if computerintelligence surpasses that of many humans?
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Questions?