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CS156 Fall 2017 Practice Midterm 1

Studying for one of my tests does involve some memorization. I believe this is an important skill. Often people waste a lot of time and fail to remember the things they are trying to memorize. Please use a technique that has been shown to work such as the method of loci. Other memorization techniques can be found off the Wiki Page for Moonwalking with Einstein. Given this, to study for the midterm I would suggest you:

  • Know how to do (by heart) all the practice problems.
  • Go over your notes at least three times. Second and third time try to see how much you can remember from the first time.
  • Go over the homework problems.
  • Try to create your own problems similar to the ones I have given and solve them.
  • Skim the relevant sections from the book.
  • If you want to study in groups, at this point you are ready to quiz each other.

The practice midterm is below. Here are some facts about the actual midterm: (a) It is closed book, closed notes. Nothing will be permitted on your desk except your pen (pencil) and test. (b) You should bring photo ID. (c) There will be more than one version of the test. Each version will be of comparable difficulty. (d) One problem (less typos) on the actual test will be from the practice test.

  1. Define the following concepts: Turing Test, Agent Function, Rational Agent.
  2. (a) Give an example problem for which there is a search strategy that is complete but not optimal. (b) Give an example problem for which there is a search strategy that is optimal when it finds a solution but not complete.
  3. Consider the tree below:
       B C
      D X
    The goal state is X. Give the order that the nodes would be visited to find a solution if an iterative deepening depth first search was used on this tree.
  4. Briefly explain the Greedy Best First Search algorithm and the `A^star`-algorithm. Give a concrete example where they would expand nodes different when performing a search.
  5. Write a python function my_mapper(f, some_list) which outputs the list derived from some_list such that the `i`th element in the result, elt_i, is computed as f(some_list[i]).
  6. Briefly explain under what conditions the `A^star`-algorithm finds the optimal solution. Give an example of a heuristic for the 8-puzzle which meets these conditions.
  7. What is the difference between hill-climbing with up to 3-restarts and 3-local beam search?
  8. Give the minimax algorithm. How would it be modified if one had a game for which one could not fully expand the game tree? Draw a game tree with a beta-cut and explain why it is a beta-cut.
  9. Define Southern California to be those counties South of an including San Luis Obispo, Kern, and San Bernadino. Express the map coloring problem using just three colors for these counties as a constraint satisfaction problem. Give an example solution.
  10. Give the AC-3 algorithm.