Review
ECE457 Applied Artificial IntelligenceSpring 2008Lecture #14
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What Is On The Final Everything that has important!
written next to it on the slides Everything that I said was
important
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What Might Be On The Final Anything in the slides
Except “What Is Not On The Final” Anything in the required readings
in the textbook
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What Is Not On The Final Examples of real applications
Dune II Traveling-wave tube IBM Deep Blue Pathfinder network Weighted Naïve Bayes Classifier Helicopter flight control Fuzzy robot navigation Neural network pixel classifier WordNet
Additional material on website Writing/debugging code
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Practice Material Examples and exercises in slides Problems at the end of each
chapter in the textbook
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Material Allowed at the Exam Pen or pencil, eraser, calculator Not allowed:
Books, notes Phones, blackberries, laptops, PDAs,
iPods, iPhones, iAnything, computers built into glasses like in Mission Impossible, or anything else electronic
Talking to other students, writing notes, sign language, smoke signals, semaphores
Cheating in general
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Summary of Course Lecture 1: Introduction to AI
Types of agents Properties of the environment
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Lecture 1: Introduction to AI Define the properties of the environment for
these problems: Robot soccer
Internet shopping (without eBay-style bidding)
Autonomous Mars rover
Theorem-solving assistant to a mathematician
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Summary of Course Lecture 2: Uninformed Search
Well-defined problem Properties of search algorithms Uninformed search
Breath-first search Uniform-cost search Depth-first search Depth-limited search Iterative deepening search
Repeated states
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Lecture 2: Uninformed Search You have a search tree with a branching
factor of b and a maximum depth of m. The depth of the shallowest goal node is d. You are considering searching the tree using either a depth-first search agent or a breath-first search agent.
Which one will have the best space complexity? Explain.
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Lecture 2: Uninformed Search You have a search tree with a branching
factor of b and a maximum depth of m. The depth of the shallowest goal node is d. You are considering searching the tree using either a depth-first search agent or a breath-first search agent.
Which one will have the best time complexity? Explain.
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Lecture 2: Uninformed Search A 3-foot-tall monkey is in a room where
some bananas are suspended from the 8-foot-high ceiling. He would like to get the bananas as quickly as possible. The room contains two stackable, movable climbable 3-foot-high crates.
Write this situation as a well-defined problem.
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Lecture 2: Uninformed Search Initial state
Action
Goal test
Cost
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Summary of Course Lecture 3: Informed Search
Informed search Greedy best-first search A* search
Heuristic functions Iterative improvement
Hill Climbing Simulated Annealing
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Lecture 3: Informed Search Given the following tree, find the optimal
path to the goal G using A* search. The value of the heuristic h is specified for each node. The costs of the edges are specified on the tree. Assume that children of a node are placed into the list in a left-to-right order, and that nodes of equal priority are extracted (for expansion) from the list in FIFO order. Write a number inside the node indicating
the order in which the nodes are expanded from the start node S, i.e. 1, 2, ….
For each node generated, write the total cost f in the appropriate location on the graph.
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1 f=4
f=
f=f=
f=f=
f=
f=
f=f=
f=f=f=
f=
S
G
1 3
22
3 1
1
21
1
122
h=4
h=2h=3
h=2
h=5 h=4 h=3
h=1
h=4
h=0
h=3
h=1
h=2
h=5
Lecture 3: Informed Search Find the optimal path to the goal G using A* search,
specifying the order in which nodes are expanded and the f-value of all generated nodes.
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Summary of Course Lecture 4: Constraint Satisfaction
Problems Constraints Defining a CSP CSP search
Backtracking search Conflict-directed backjumping Heuristics Forward checking AC-3 algorithm
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Lecture 4: CSP Using the most-constrained-variable CSP
heuristic, colour the adjacent map using the colours Blue, Red and Green. Show your reasoning at each step of the algorithm. Proceed in the following manner: After assigning a colour to a region, and before
choosing the next region to colour, apply the forward checking algorithm and show its results. Then choose the next region to colour using the most-constrained-variable heuristic, etc.
At each step, show the domains of each region and justify the choice of the next region to colour.
AB
C
D
E
F
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Variables marked * have been assigned
A* = {Green}B* = {Red}C = {Red, Blue, Green}D = {Red, Blue, Green}E = {Red, Blue, Green}F = {Red, Blue, Green}
Lecture 4: CSP
AB
C
D
E
F
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Summary of Course Lecture 5: Game Playing
Payoff functions Minimax algorithm Alpha-Beta pruning Non-quiescent positions & horizon
effect Expectiminimax
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Lecture 5: Game Playing Consider the following game tree.
The payoff value of each leaf is written under that node. Apply the Minimax algorithm to obtain
the value of each non-leaf node. Apply Alpha-Beta Pruning to the game
tree. Find which nodes will be pruned. For each one, identify and explain the value of alpha and beta to show why it is pruned.
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Lecture 5: Game Playing
4 8 9 -2 2
A
ED
C
B
MAX
MIN
F
G
H
I
J K
MAX
H
-1 -8 5
L
M N
A B C F H I L
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Summary of Course Lecture 6: Logical Agents
Language, syntax, semantics Propositional logic
Propositional symbols and logical connectives
Inference with truth tables Inference with Resolution
Conversion to CNF Inference with Modus Ponens
Horn clauses Forward chaining Backward chaining
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Summary of Course Lecture 7: First-Order Logic
First-Order Logic Constants, predicates, functions Universal and existential quantifiers Converting English sentences
Inference with propositionalization Inference with Generalized Modus
Ponens Unification algorithm
Inference with Resolution Conversion to CNF
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Lecture 7: First-Order Logic Represent the following sentences in FOL using:
Take(s,c,t), Pass(s,c,t), Score(s,c,t), Student(s), French, Greek, Spring2001
Some students took French in spring 2001
Every student who takes French passes it
Only one student took Greek in Spring 2001
The best score in Greek is always higher than the best score in French
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Lecture 7: First-Order Logic Convert this FOL sentences to
Conjunctive Normal Form. Show all steps of the conversion.
x [y F(y) G(x,y)] y G(y,x)
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Lecture 7: First-Order Logic Find the most general unifier, if it exists. p = F(A,B,B)
q = F(x,y,z)
p = F(y,G(A,B))q = F(G(x,x),y)
p = F(G(y),y)q = F(G(x),A)
p = F(G(y),y)q = F(x,x)
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Lecture 7: First-Order Logic Given the following KB:
Faster(x,y) Faster(y,z) Faster(x,z) Pig(x) Slug(y) Faster(x,y) Buffalo(x) Pig(y) Faster(x,y) Slug(Slimm) Pig(Pat) Buffalo(Bill)
Is Bill faster than Slimm, using forward chaining
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Lecture 7: First-Order Logic Given the following KB:
Slimy(x) Creepy(x) Slug(x) Pig(x) Slug(y) Faster(x,y) Slimy(Slimm) Creepy(Slimm) Pig(Pat)
Is Pat faster than Slimm, using backward chaining
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Lecture 7: First-Order Logic
Given the following KB: Person(Marcus) Pompeian(Marcus) ¬Pompeian(x1) Roman(x1) ¬Roman(x2) Loyal(x2,Caesar) Hate(x2, Caesar) ¬Person(x3) ¬Ruler (x4) ¬Assasinate(x3, x4) ¬Loyal(x3,x4)
Does Marcus hate Caesar, using resolution
Ruler(Caesar) Assasinate(Marcus, Caesar)
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Summary of Course Lecture 8: Uncertainty
Marginalization Bayes’ Theorem Chain rule Independence and conditional
independence Naïve Bayes Classifier
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Lecture 8: Uncertainty You tested positive for a disease. The test’s
results are accurate 99% of the time. However, the disease only strikes 1 out of 10000 people. What’s the probability that you have the disease?
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Lecture 8: Uncertainty
Given the following police data, create a Naïve Bayes Classifier for stolen cars, and compute the probability that a domestic red SUV is stolen.
C T O S
Red Sports
Domestic
Stolen
Red Sports
Domestic
Not
Red Sports
Domestic
Stolen
Yellow
Sports
Domestic
Not
Yellow
Sports
Imported
Stolen
Yellow
SUV Imported
Not
Yellow
SUV Imported
Stolen
Yellow
SUV Domestic
Not
Red SUV Imported
Not
Red Sports
Imported
Stolen
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Summary of Course Lecture 9: Probabilistic Reasoning
Bayesian Network Connections and D-Separation Inference
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Lecture 9: Probabilistic Reasoning
Consider this Bayesian network. Write the factored expression for
the joint probability distribution P(A, B, C, D, E, F) which is represented by this network.
Which variables are independent (d-separate) of C if: B is known. A is known. D and E are both know.
A B
C
E
D
F
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Lecture 9: Probabilistic Reasoning
Given the following values, what is the posterior probability of F given that B is true? P(D|B) = 0.8
P(D|B) = 0.4P(F|D) = 0.75P(F|D) = 0.6
A B
C
E
D
F
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Summary of Course Lecture 10: Decision Making
Maximum Expected Utility Utility Expected utility
Decision network Optimal policy
Computing the optimal policy Value of information
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Lecture 10: Decision Making
A C
D
UB
A P(B)
F 0.7
T 0.4
C P(D)
F 0.5
T 0.8
B C D U
F F F 0.6
F F T 0.2
F T F 1
F T T 0.4
T F F 0.8
T F T 0.2
T T F 0.7
T T T 0.1
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Summary of Course Lecture 11: Introduction to Learning
For all learning algorithms Training data Objective of learning Evaluation General algorithm
Precision and recall Overfitting and n-fold cross-validation K-Means Q-Learning Exploration function
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Summary of Course Lecture 12: Introduction to Soft
Computing Fuzzy logic
Fuzzy sets, fuzzy membership functions, membership degree
Fuzzy rules Artificial neural networks
Artificial neuron Perceptron network
Genetic algorithms Individuals Operators: crossover, mutation, selection Search algorithm
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Summary of Course Lecture 13: Introduction to
Ontologies Objects, Categories, Relations,
Attributes Inheritance
Problems
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