artificial intelligence - ntnuweb.ntnu.edu.tw/~tcchiang/ai/0_syllabus.pdf · 11 21 artificial...
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Artificial Intelligence, Spring, 2010
Artificial Intelligence
Instructor: Tsung-Che [email protected]
Department of Computer Science and Information EngineeringNational Taiwan Normal University
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Texts
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Grading Policy
In-class exercises & take-home assignments(65% ~ 85%) C/C++ programming skill is required. There will be at least 4 take-home assignments. Late submissions: 1~3 days with 10% penalty, 4~7 days
with 20% penalty The submission with 8-day or longer delay will not be
accepted.
Final exam (20 ~ 0%) Class participation (15%)
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Syllabus Introduction to Intelligent Agents Search
Blind Search Informed Search Constraint Satisfaction Problem Adversarial Search
Logic Propositional Logic
Soft Computing Fuzzy Systems Artificial Neural Network Metaheuristics
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Agents
An agent is anything that can be viewed asperceiving its environment through sensors andacting upon that environment thorough actuators.
Artificial Intelligence: A Modern Approach, 2nd ed., Figure 2.9
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Agents
What is a rational agent?Task environments (problems)
Definition Performance, Environment, Actuator, Sensor
Properties Observable, Deterministic, Static, etc.
Agent structureSimply reflexModel-basedGoal-basedUtility-based
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Agents
Utility-based agent
Artificial Intelligence: A Modern Approach, 2nd ed., Figure 2.14
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972 Homework 1
Best Cleaner
Thanks to Mr. Shi-Yau Yu for the interface.
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Problem Solving
Artificial Intelligence: A Modern Approach, 2nd ed., Figure 3.1
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Problem Solving
Example problems
8-queens
8-puzzle
Vacuum world
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Uninformed Search Strategies
Breadth-first searchUniform-cost searchDepth-first searchIterative deepening searchBidirectional search
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972 Homework 2
Missionaries and Cannibals Problemhttp://www.learn4good.com/games/puzzle/boat.htm
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Informed Search Strategies
Greedy best-first searchA* searchMemory-bounded heuristic search
Artificial Intelligence: A Modern Approach, 2nd ed., Figure 4.3 & 4.7
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Informed Search Strategies
Hill climbingOnline search
(972 Homework 3)
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Constraint Satisfaction Problem
Backtracking searchVariable & value ordering Constraint propagation Intelligent backtracking
Local searchProblem structure
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972 Homework 4
http://www.agame.com/game/Connectors.html
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Adversarial Search
Artificial Intelligence: A Modern Approach, 2nd ed., Figure 6.1
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Adversarial Search
Minimax algorithmAlpha-beta pruningImperfect, real-time decisions
Evaluation function & cut-off test
Games including an element of chance
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Logical Agents
Artificial Intelligence: A Modern Approach, 2nd ed., Figure 7.1 & 7.2
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Propositional Logic
Sentence AtomicSentence | ComplexSentence
AtomicSentence True | False | Symbol
Symbol P | Q | R | …
ComplexSentence Sentence| (Sentence Sentence)| (Sentence Sentence)| (Sentence Sentence)| (Sentence Sentence)
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Propositional Logic
ReasoningModus PonensAnd-Elimination Resolution Forward/Backward chaining Backtracking Local search
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Fuzzy Systems(a) Boolean Logic. (b) Multi-valued Logic.
0 1 10 0.2 0.4 0.6 0.8 100 1 10
Degree of MembershipFuzzy
MarkJohnTom
Bob
Bill
11100
1.001.000.980.820.78
Peter
Steven
MikeDavid
ChrisCrisp
1
0000
0.240.150.060.010.00
Name Height, cm
205198181
167
155152
158
172179
208
150 210170 180 190 200160
Height, cmDegree ofMembership
Tall Men
150 210180 190 200
1.0
0.0
0.2
0.4
0.6
0.8
160
Degree ofMembership
170
1.0
0.0
0.2
0.4
0.6
0.8
Height, cm
Fuzzy Sets
Crisp Sets
Artificial Intelligence: A Guide to Intelligent Systems, 2nd ed., Figure 4.1 & 4.2, Table 4.1
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Fuzzy Systems
150 210170 180 190 200160Height, cm
Degree ofMembership
Tall Men
150 210180 190 200
1.0
0.0
0.2
0.4
0.6
0.8
160
Degree ofMembership
Short Average ShortTall
170
1.0
0.0
0.2
0.4
0.6
0.8
Fuzzy Sets
Crisp Sets
Short Average
Tall
Tall
Artificial Intelligence: A Guide to Intelligent Systems, 2nd ed., Figure 4.3
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Fuzzy Systems
Rule 1:IF Distance is ShortAND Health is GoodTHEN Action is Chasing
Rule 2:IF Distance is LongAND Health is GoodTHEN Action is Do Nothing
Rule 3:IF Distance is ShortAND Health is BadTHEN Action is Escaping
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Fuzzy Inference
MamdaniSugeno
Crisp Inputy1
0.1
0.71
0y1
B1 B2
Y
Crisp Input
0.20.5
1
0
A1 A2 A3
x1
x1 X(x = A1) = 0.5(x = A2) = 0.2
(y = B1) = 0.1(y = B2) = 0.7
A 31
0 X
1
y10 Y
0 .0
x 1 0
0 .1C 1
1
C 2
Z
1
0 X
0 .2
0
0 .2 C 11
C 2
Z
A 2
x1
R ule 3 :
A 11
0 X 0
1
Zx1
T H E N
C 1 C 2
1
y1
B 2
0 Y
0 .7
B 10 .1
C 3
C 3
C 30 .5 0 .5
O R(m a x )
A N D(m in )
O R T H E NR ule 1 :
A N D T H E NR ule 2 :
IF x is A 3 (0 .0 ) y is B 1 (0 .1 ) z is C 1 (0 .1 )
IF x is A 2 (0 .2 ) y is B 2 (0 .7 ) z is C 2 (0 .2 )
IF x is A 1 (0 .5 ) z is C 3 (0 .5 )
Artificial Intelligence: A Guide toIntelligent Systems, 2nd ed., Figure4.10
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Artificial Neural Networks
Threshold
Inputs
x1
x2
Output
Y
HardLimiter
w2
w1
LinearCombiner
Soma Soma
Synapse
Synapse
Dendrites
Axon
Synapse
Dendrites
Axon
Input Layer Output Layer
Middle Layer
Inp
ut
Sig
na
ls
Ou
tpu
tS
ign
als
Biological Neural Network Artificial Neural NetworkSomaDendriteAxonSynapse
NeuronInputOutputWeight
Artificial Intelligence: A Guide to Intelligent Systems, 2nd ed., Figure 6.1, 6.2, 6.5
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Artificial Neural Networks
PerceptronBack-propagation networkHopfield networkBidirectional associative memorySelf-organizing map
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Metaheuristics
Evolutionary computationAnt colony optimizationParticle swarm optimizationTabu search
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Genetic Algorithms
Stop?
Mating selection
Reproduction
Environmentalselection
Y
Initial Population
Final Population
Evaluation
NEvaluation
nextgeneration
generation 1
offspring
•evaluation•mating selection•reproduction
•evaluation•environmental
selection
generation 2