CS 458/558 - Problem Set 1

Assigned:	Friday January 19
Deadline:	Friday February 9, 11:59pm

One of the earliest domains for automated decision systems was game playing. We will discuss chess and checkers, including Art Samuel's seminal checkers program.

For this problem set, we look at a common casino card game: blackjack. We have implemented a simple version in Python, using a Python development environment, codeskulptor.

Blackjack implementation in codeskulptor. Click the run button in the top left.

Codeskulptor site. Check out the docs and demos. Codeskulptor has very good python reference materials, as well as tutorials for learning python.

Raw python code for blackjack. Note: this will likely not run properly outside the CodeSkulptor environment.

Code skeleton for writing a player in python3. Based on the CodeSkulptor implementation.

This program implements a pretty vanilla form of blackjack. You cannot double down, buy insurance, or count cards. There is one deck and it is reshuffled before every hand. The dealer has no discretion. She has to stay on 17 or higher.

For this code, the human is in the loop. The player has to click the hit or stand buttons. Your job is to automate this process.

Create a function hitme(playerhand, dealerfacecard) which returns a boolean value, true or false, specifying whether the player should ask for another card or not. That is, after the cards are initially dealt, the program will call hitme and either ask for another card or stand - untouched by human hands.

Even though the example code uses graphics, your code should not. It should be built for speed, not looks.

The hitme function will simply perform a table look-up. You will have a 2 dimensional matrix comprising the optimal strategy for all possible combinations of player hands and dealer face cards. The values in the matrix will simply be true or false. For example,

hitme(12, 1) ==> true

If your hand value is 12 and the dealer has an ace, you should ask for another card.

hitme(18, 4) ==> false

If your hand value is 18 and the dealer shows a four, you should stay put.

Actually, it is not up to you to populate the values of the table through dint of insight. Rather, you will write another function sim(trials), which performs Monte Carlo simulation.

The sim function will perform a boatload of trials and produce as output a matrix of probabilities. For example, in the cell corresponding to a player hand of 18 and a dealer face card of 4, the value may be 235 / 978 meaning that this combination occurred 978 times in the simulation and in only 235 times did the player win if she asked for another card. You would then convert that matrix to boolean values, based on some threshold ratio value. The normal approach would be to use 50%, but you may decide to use a different value.

Once you have your hitme table installed, run your own simulated games, keeping track of the win/loss ratio. Report your results for simulations of at least 100,000 hands.

Finally, write a function play(trials) which will play the given number of hands and return your overall winning percentage.

You should submit one file: player.py In addition, you should hand in a README file which explains what function does what, as well as a transcript including the output of Monte Carlo simulation, and the summary output of 100,000 game playing trials - how many games the player won. In the past when giving assignments involving automated game playing, I have awarded a prize to the most successful program. Keep that in mind.

Submitting through Gradescope

In order to facilitate automated grading and regrade requests, this assignment will be submitted through Gradescope on the Course Assignments page.

You must submit both the completed player.py and README files. NB: Your README must still include the descriptions, transcript, and summary output as described above.

The autograder will test each of the sim, hitme, and play functions, and public test cases will be available to confirm your outputs meet the specifications.