You should turn in four files: (1) a file named a1.py of function definitions, (2) a file a1ExamplesPlus.txt of example calls, results and answers to questions, (3) a file containing the Python code for your conversational agent (named as described in the instructions), and (4) a text file sampleConversation.txt containing a sample dialog between your agent and your partner's agent (see instructions for details).

For the file of examples, you can use a combination of input and output together with comment lines (beginning with a pound sign) to show that you have verified each of your answers on the computer. For each function, show a demonstration on the same example shown on this page, and two additional examples: one shorter (or smaller), and one longer (or larger) and more interesting (use your imagination).
 

1. five_x_cubed_plus_2(3) -> 137

2. triple_up([2, 5, 1.5, 100, 3, 8, 7, 1, 1, 0, -2])  ->  [[2, 5, 1.5], [100, 3, 8], [7, 1, 1], [0, -2]]

3. mystery_code("abc Iz th1s Secure? n0, no, 9!") -> "VWX dU OC1N nZXPMZ? I0, IJ, 9!"

 (Hint: If a character of the input is alphabetic, then it undergoes a
  change of case as well as a mapping to a different place in the alphabet.)

4 future_tense(['Yesterday', 'I', 'ate', 'pasta', 'and', 'today', 'I', 'am', 'having', 'soup']) ->
 ['Tomorrow', 'I', 'will', 'eat', 'pasta', 'and', 'tomorrow', 'I', 'will', 'be', 'having', 'soup']

  future_tense(['Life', 'is', 'good', 'now']) ->
 ['Life', 'will', 'be', 'good', 'tomorrow']

Note: In the future-tense function, handling of negation with the verb do is optional, not required. For example "We didn't eat." should map to "We will not eat.", but you don't have to handle that kind of sentence. Also, you don't have to handle the use of do as a helping verb, as in "We do eat meat.".

The above exercises will be graded with a combination of autograder and manual grading. Exercises 1 and 2 are worth 5 points each. Three points will be awarded for passing the instructors' autograder tests, and one point will awarded for giving each required example in your a1examplesPlus.txt file. Exercises 3 and 4 are worth 10 points each, with 6 of the points given for passing the autograder tests and 2 points for each required example.

A simple "sanity-check" testing program is available that might help you realize that you have a spelling error in a function name or that your functions do not return values of the proper type: a1_fn_sig_checker.py. Note that this file is not an autograder, and it does NOT test whether your functions compute the correct values.

• You are welcome to use rules from the Shrink3.py program, but each rule must be modified so that the response will be different.
• Incorporate a "memory" feature into your agent so that the conversation can return to some topic introduced earlier by the user. Make it work in a manner consistent with the character of your agent. (5 points).
• Incorporate a "cycle" feature into your agent so that when a given rule fires repeatedly in a session, the response is not always the same but changes in a cyclical pattern. At least 2 of your agent's rules should use this feature with at least 2 alternative responses each. (5 points)
• Make up at least one rule that uses a random-choice feature to select a response form. (5 points)
• There should be at least 15 rules in your program.
• Your program should be ready to be used as a module in another program that runs your agent with another agent in a dialog. The interface will consist of three functions that you need to write: one called introduce(), one called respond(theInput), and one called agentName().
   
  The introduce function should return a string representing a message that tells the name of the agent, what the agent represents and the name and UWNetID of you the programmer. For example, it might return a string containing:

  My name is Rusty Sales, and I sell junky cars.
  I was programmed by Jenny Chrysler. If you don't like
  the way I deal, contact her at jchrysler@u.
  How can I help you?
  

  The respond function should work almost the same way as the one in Shrink3.py. But there are two important differences. First, the function will take one argument: the input string. It should compute the wordlist and mapped_wordlist values at the beginning of the function body instead of receiving those as inputs as in Shrink3.py. Second, instead of printing out its response, your respond function should return it as a string. This will allow the other agent to receive it as input in the joint-dialog program.
   
  The agentName function should return (as a string) a short nickname for your agent. This will be useful in printing out a prompt-like identifier when showing lines of a conversation among different agents. For example, the function might return Rusty for example above.
   
  You can test out your compliance to this interface by downloading the dialog.py and minimal agent programs.
• Name your agent's Python file in the following way, so that we can keep track of the different agents: YourUWNetID_agent.py, where YourUWNetID represents your UWNetID code (i.e., your email user name within the u.washington.edu domain. For one thing, this will guarantee that each of our agents is implemented in a file with a unique name. It will also give the graders an easy way to find your agent within a group, if needed.
• Provide a comment in the code for each of your production rules.
• In addition to your Python file, turn in a text file sampleConversation.txt that includes at least 10 turns (and no more than 15) of the conversation for each agent, where Agent A is your agent and agent B is the agent of your partner. You can hand-edit the file with some comments -- ALL IN CAPS -- that point out places where your agent is using its MEMORY FEATURE, CYCLE FEATURE, and RANDOM-CHOICE feature. To help you create the conversation, you can use the dialog.py and Hearnone.py programs mentioned earlier.
• If there are no repeated responses in your sample conversation, and the conversation makes sense, we will award 5 points of extra credit to the author of each agent. However, if either agent repeats a response in the conversation (for example, it says "You seem quite negative" and then later says that again), then no extra credit will be awarded to either author.