CSC 550: Introduction to Artificial Intelligence
Fall 2008

HW2: AI programming and Search

PART 1: Eliza

Copy the Eliza program from eliza.scm and make the following modifications:

  1. The Eliza rules currently include a rule that looks for the word "computer", and responds accordingly. However, this rule will not catch the plural "computers". Add a rule to the database that simiarly responds to user input containing "computers".
  2. Add a rule to the Eliza rule database that recognizes the phrase "I hate ___" in the user's input. When this phrase is found anywhere in the user input, Eliza should respond with either "Hate is such an intense emotion" or else a response of the form "What is it that you hate about ___".
  3. Add at least three more rules to the Eliza rule database of your own design.
  4. As it is currently written, the only way to terminate the Eliza program is to issue a break (by clicking on the Break button). Augment the program so that any user input that contains the word "bye" will result in the response "Have a nice day." and will terminate the program.

PART 2: State Spaces & Search

Scheme provides a utility function, time, that can be useful in profiling code. The time function, when given an expression as input, evaluates that expression and reports the number of milliseconds of CPU time taken for the evaluation. It even identifies the amount of CPU time taken by garbage collection, so that number can be subtracted out to determine the amount of time spent doing actual computation.

For example, the call (time (bfs '(0 0) '(2 0))) would report how long it took to solve the Jugs Puzzle using breadth-first search. Note, however, that the shortest time reportable by the time function is 10 ms. If a search succeeds in finding a solution in less than 10 ms, then it will register as no time at all. If this occurs, time multiple calls to the function and divide by the number of repetitions to get a more accurate timing. For example, the following call would time how long it takes for breadth-first search to solve the Jugs Puzzle five times.

(time (begin (bfs '(0 0) '(2 0)) (bfs '(0 0) '(2 0)) (bfs '(0 0) '(2 0)) (bfs '(0 0) '(2 0)) (bfs '(0 0) '(2 0))))

  1. Consider each of the three state space problems discussed in class:

    Using the time function and the implementations in search.scm, compare the performances of DFS, DFS-nocycles, BFS, BFS-nocycles, and DFS-deepening on each of these problems. That is, when the strategy produces an answer, report the execution time (not counting garbage collection). Comment on the apparent tradeoffs between the strategies.

  2. Consider the problem of traversing a maze, from one opening (identified as the Start) to another opening (identified as the Exit). For example, the diagram on the left shows an 8x9 maze, where the Start is at the upper-left corner, the Exit is at the lower-right corner, and walls are shown as black rectangles. To the right is a Scheme list structure that represents this maze.

    (define MAZE '("*********" "S *" "* ** * *" "* ** *" "* * *****" "* * *" "* * ** E" "*********"))

    For maze traversals, a state space can be defined in which a state is defined by the coordinates of the person in the maze. For the above maze, we might identify the Start state as (1 0), designating the row at index 1 and the column at index 0. Likewise, the goal (Exit) state for this maze would be identified as (6 8).

    The file maze.scm contains a Scheme definition of the state space for this problem. Using this implementation, compare the performances of the uninformed search strategies on this maze and comment on the apparent tradeoffs.

  3. Similarly, compare the performances of the uninformed strategies on the following maze:

    S E

  4. The maze.scm file defines a heuristic for rating possible moves when searching a maze. A state is assigned a heuristic value based on its row/column distance from the goal state. For example, if the goal state is at (6 5), then the state (3 3) would have heuristic value -5 (since it is off by and 3 rows and 2 columns) and the state (7 3) would have heuristic value -3 (since it is off by 1 row and 2 columns).

    Using this hueristic function, attempt to solve the mazes from the last two problems using hill-climbing and best-first search (defined in heuristics.scm). If a strategy is unable to solve the problem, explain why. If it is successful, time its execution and compare its efficiency with the uninformed strategies.