1. Review ECE457 Applied Artificial Intelligence Spring 2008 Lecture #14

2. ECE457 Applied Artificial Intelligence R. Khoury (2008)Page 2 What Is On The Final Everything that has important! written next to it on the slides Everything that I said was important

3. ECE457 Applied Artificial Intelligence R. Khoury (2008)Page 3 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

4. ECE457 Applied Artificial Intelligence R. Khoury (2008)Page 4 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

5. ECE457 Applied Artificial Intelligence R. Khoury (2008)Page 5 Practice Material Examples and exercises in slides Problems at the end of each chapter in the textbook

6. ECE457 Applied Artificial Intelligence R. Khoury (2008)Page 6 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

7. ECE457 Applied Artificial Intelligence R. Khoury (2008)Page 7 Summary of Course Lecture 1: Introduction to AI Types of agents Properties of the environment

8. ECE457 Applied Artificial Intelligence R. Khoury (2008)Page 8 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

9. ECE457 Applied Artificial Intelligence R. Khoury (2008)Page 9 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

10. ECE457 Applied Artificial Intelligence R. Khoury (2008)Page 10 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.

11. ECE457 Applied Artificial Intelligence R. Khoury (2008)Page 11 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.

12. ECE457 Applied Artificial Intelligence R. Khoury (2008)Page 12 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.

13. ECE457 Applied Artificial Intelligence R. Khoury (2008)Page 13 Lecture 2: Uninformed Search Initial state Action Goal test Cost

14. ECE457 Applied Artificial Intelligence R. Khoury (2008)Page 14 Summary of Course Lecture 3: Informed Search Informed search Greedy best-first search A* search Heuristic functions Iterative improvement Hill Climbing Simulated Annealing

15. ECE457 Applied Artificial Intelligence R. Khoury (2008)Page 15 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.

16. ECE457 Applied Artificial Intelligence R. Khoury (2008)Page 16 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.

17. ECE457 Applied Artificial Intelligence R. Khoury (2008)Page 17 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

18. ECE457 Applied Artificial Intelligence R. Khoury (2008)Page 18 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. A B C D E F

19. ECE457 Applied Artificial Intelligence R. Khoury (2008)Page 19 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 A B C D E F

20. ECE457 Applied Artificial Intelligence R. Khoury (2008)Page 20 Summary of Course Lecture 5: Game Playing Payoff functions Minimax algorithm Alpha-Beta pruning Non-quiescent positions & horizon effect Expectiminimax

21. ECE457 Applied Artificial Intelligence R. Khoury (2008)Page 21 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.

22. ECE457 Applied Artificial Intelligence R. Khoury (2008)Page 22 Lecture 5: Game Playing 489-22 A ED C B MAX MIN F G H I JK MAX H -85 L MN ABCFHIL

23. ECE457 Applied Artificial Intelligence R. Khoury (2008)Page 23 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

24. ECE457 Applied Artificial Intelligence R. Khoury (2008)Page 24 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

25. ECE457 Applied Artificial Intelligence R. Khoury (2008)Page 25 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

26. ECE457 Applied Artificial Intelligence R. Khoury (2008)Page 26 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)

27. ECE457 Applied Artificial Intelligence R. Khoury (2008)Page 27 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)

28. ECE457 Applied Artificial Intelligence R. Khoury (2008)Page 28 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

29. ECE457 Applied Artificial Intelligence R. Khoury (2008)Page 29 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

30. ECE457 Applied Artificial Intelligence R. Khoury (2008)Page 30 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)

31. ECE457 Applied Artificial Intelligence R. Khoury (2008)Page 31 Summary of Course Lecture 8: Uncertainty Marginalization Bayes’ Theorem Chain rule Independence and conditional independence Naïve Bayes Classifier

32. ECE457 Applied Artificial Intelligence R. Khoury (2008)Page 32 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?

33. ECE457 Applied Artificial Intelligence R. Khoury (2008)Page 33 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. CTOS RedSportsDomesticStolen RedSportsDomesticNot RedSportsDomesticStolen YellowSportsDomesticNot YellowSportsImportedStolen YellowSUVImportedNot YellowSUVImportedStolen YellowSUVDomesticNot RedSUVImportedNot RedSportsImportedStolen

34. ECE457 Applied Artificial Intelligence R. Khoury (2008)Page 34 Summary of Course Lecture 9: Probabilistic Reasoning Bayesian Network Connections and D-Separation Inference

35. ECE457 Applied Artificial Intelligence R. Khoury (2008)Page 35 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.

36. ECE457 Applied Artificial Intelligence R. Khoury (2008)Page 36 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) = 0.4 P(F|D) = 0.75 P(F|  D) = 0.6

37. ECE457 Applied Artificial Intelligence R. Khoury (2008)Page 37 Summary of Course Lecture 10: Decision Making Maximum Expected Utility Utility Expected utility Decision network Optimal policy Computing the optimal policy Value of information

38. ECE457 Applied Artificial Intelligence R. Khoury (2008)Page 38 Lecture 10: Decision Making A C D U B AP(B) F0.7 T0.4 CP(D) F0.5 T0.8 BCDU FFF0.6 FFT0.2 FTF1 FTT0.4 TFF0.8 TFT0.2 TTF0.7 TTT0.1

39. ECE457 Applied Artificial Intelligence R. Khoury (2008)Page 39 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 Page 43

40. ECE457 Applied Artificial Intelligence R. Khoury (2008)Page 40 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 Page 44

41. ECE457 Applied Artificial Intelligence R. Khoury (2008)Page 41 Summary of Course Lecture 13: Introduction to Ontologies Objects, Categories, Relations, Attributes Inheritance Problems Page 45