CS156 Spring 2015Practice Final

To study for the final I would suggest you: (1) Know how to do (by heart) all the practice problems. (2) Go over your notes at least three times. Second and third time try to see how much you can remember from the first time. (3) Go over the homework problems. (4) Try to create your own problems similar to the ones I have given and solve them. (5) Skim the relevant sections from the book. (6) If you want to study in groups, at this point you are ready to quiz each other. The practice final is below. Here are some facts about the actual final: (a) It is comprehensive (b) It is closed book, closed notes. Nothing will be permitted on your desk except your pen (pencil) and test. (c) You should bring photo ID. (d) There will be more than one version of the test. Each version will be of comparable difficulty. (e) It is 10 problems, 6 problems will be on material since the lecture before the midterm, four problems will come from the topics covered prior to the midterm. (f) Two problems will be exactly (less typos) off of the practice final, and one will be off of practice midterm.

1. Give the DPLL algorithm. Give an example set of clauses and situation where the pure symbol heuristic could be applied during the execution of DPLL. You should carefully your example works.
2. Consider the first-order formula `forall x exists y (x = S(S(0)) cdot y vv x = S(S(0)) cdot y + S(0))`. What are the constants, variables, function symbols, and predicates in this formula? Carefully give a model and variable assignment under which this formula is satisfied.
3. Consider the task of brushing your teeth in the morning, this might involve applying toothpaste to a toothbrush, wetting the toothbrush, brushing your teeth and rinsing. Model this problem as a PDDL planning problem. Give an example solution plan.
4. Give and briefly explain the GraphPlan algorithm presented in class.
5. Explain and give an example of how composite objects might be modeled using the PartOf relation in a Knowledge Representation system.
6. Explain how we might be able to calculate `\vec(P)(Cavity |t\o\othache)`, given only knowledge of `\vec(P)(Cavity, t\o\othache)`.
7. Give an example with pseudo-code of an unsupervised learning algorithm we covered in class.
8. Explain and give an example of how our decision tree learning algorithm selects an attribute to be the root of the subtree that it is currently working to construct.
9. Give a choice of weights and an activation function to make a perception on 10 inputs which returns 1 if more than 7 inputs are on, and 0 otherwise.
10. Briefly explain the perceptron learning algorithm described in class.