Some sample exam 2 questions: 1. Briefly define the following terms: Eager learning Lazy Learning Curse of dimensionality kd Tree Single Point Crossover Two Point Crossover Uniform Crossover Point Mutation The Baldwin Effect Inverted Deduction Unsupervised learning Clustering Algorithm Dendogram Control Learning Delayed Reward Discounted Future Reward Markov Decision Process Analytical Learning Chunking Impasse 2. How does a k-Nearest Neighbor learner make predictions about new data points? How does a distance-weighted k-Nearest Neighbor learner differ from a standard k-Nearest Neighbor learner? What is locally weighted regression? 3. How does a Radial Basis Function network work? How does a kernel function work? 4. What is Case-Based Reasoning (CBR)? Give an example of how CBR might be used to solve a new problem. 5. How are concepts represented in a genetic algorithm? Give an example of of concept represented in a GA. 6. What operators are used in a genetic algorithm to produce new concepts? Give an example of a mechanism that can be used to judge a GA concept. 7. Give pseudo-code for a general genetic algorithm. Make sure to outline the way concepts are represented, the operators used to create new concepts, how concepts are chosen to reproduce, and how concepts are evaluated. 8. Give two different mechanisms for selecting which members of a GA population should reproduce. What are the advantages and disadvantages of your mechanisms? 9. How does genetic programming work? How is a genetic program defined? What genetic operators can be applied to a genetic program? 10. How does the sequential covering algorithm work to generate a set of rules for a concept? 11. How does FOIL work to generate first-order logic rules for a concept? 12. What does it mean to view induction as inverted deduction? Give a deduction rule and explain how that rule can be inverted to induce new rules. 13. What are the two main approaches for generating clusters? Explain in general terms how these approaches work. 14. List two methods that could be used to estimate the validity of the results of a clustering algorithm. Explain how these methods work. 15. Explain how the following clustering methods work: Agglomerative Single Link Agglomerative Complete Link K-Means 16. A distance measure is important both in memory-based reasoning methods such as the k-nearest neighbor method and in clustering. Why is it so critical in these methods. In which is it possible to "learn" to do a better job of measuring the distance between points? Why? 17. Give pseudo-code for the learning cycle of a Q learner. What is the update rule for a deterministic world? How about a non-deterministic world? 18. How are the V(s) and Q(s,a) functions related in Q learning? What are the advantages of using the Q function over the V function? 19. How does Temporal Difference Learning relate to Q learning? 20. What is the difference between analytic or speedup learning and inductive learning? Give an example of each type of learning. 21. Give an example of an explanation of a concept that might be used by an explanation-based learner. What could be learned from this explanation? How does the concept of operationalization relate to what is learned? 22. What are some of the problems that can result from using a domain theory in an explanation-based learner? How can we address these problems? 23. What is the utility problem in analytical learning? How can we define utility? 24. What does it mean when we say that PRODIGY learns control knowledge? What is the advantage of learning control knowledge over adding new rules to a domain theory? 25. How does chunking work in SOAR? When does a SOAR system try to create a chunk?