CS302 -- Project 3 -- Superball!


Tue Nov 27 16:37:58 EST 2007. Last revision: Wed Feb 6 11:50:52 EST 2020 (by SJE)

What you hand in

My former UT students are pretty insistent we keep the CS302 Superball! challenge... and Dr. Plank wants me to use all of his labs/notes (if I want), so here it is.

As in the Fall (and previous iterations), this project must be done alone.

You need to submit the source code for two programs: sb-analyze.cpp and sb-play.cpp as a tar archive (.tar) on Canvas (as you have for Challenges).

Also

Every year, someone asks Dr. Plank for the source to sb-player. He does not give it out since its "too easy" in his words to solve the lab with it. There are, however, various implementations (see below) and we can ask Dr. Plank to try and make an sb-player binary for your machine. Let us know.

There is an sb-player binary for macs in sb-player-mac.

Plus, in 2015, Alex Teepe wrote a multiplatform Superball player to share. Neither Dr. Plank nor I have not tried it, but please do. Thanks, Alex!

https://drive.google.com/file/d/0B4rzPrfwFCsKbUpwd21pMlgtc1E/view.


Disjoint Sets

Use the disjoint sets code from the lecture note directory in the course git repo. When you instantiate your disjoint set instance, use "new DisjointSetByRankWPC". Since you don't use the other implementations, you don't need to compile with them.

If you don't understand how to compile your program correctly, please ask the TA's or ask on Piazza. DO NOT COPY THE DISJOINT SET CODE AND INCLUDE IT WITH YOUR PROGRAM.


Superball

Superball is a simplistic game that was part of a games CD for Dr. Plank's old Windows 95 box. It works as follows. You have a 8x10 grid which is the game board. Each cell of the game board may be empty or hold a color:

The board starts with five random colors set. On your turn, you may do one of two things:

Dr. Plank has a tcl/tk/shell-scripted Superball player at /home/jplank/Superball. Simply copy that directory to your home directory:

UNIX> cp -r /home/jplank/Superball $HOME

Then you can play it with ~/Superball/Superball. The high score probably won't work -- you'll have to change the open command in the file hscore to the name of your web browser.

Let's look at some screen shots. Suppose we fire up Superball:


The "goal" cells are marked with asterisks, and there are five non-empty cells. Our only legal action is to swap two cells -- Dr. Plank swaps cells [3,6] and [5,8] below. This will make those two blue cells contiguous. In the game, we can do that by clicking on the two cells that we want to swap. Afterwards, five new cells are put on the screen. Here's the screen shot:


Dr. Plank does a bunch more swaps and ends up with the following board:


We can score the green cells by clicking on cell [2,1], [3,0], [3,1] or [4,0] and then clicking "Collect". This will score that group of eight green squares, which gets us 48 points (8*6), and three new cells will be added:


There are no cells to score here (the blues ones in the lower right-hand part of the board only compose a group of four). So Dr. Plank now reverts to swapping. Suppose we keep doing this until we reach:

We're in trouble. Dr. Plank has now got these beautiful groups of red, green and purple cells, but he can't score any of them because they are not connected to a goal. Dang. We can only score those two groups of blue cells. When Dr. Plank does that, he is only left with four open squares, and we can't score anything:


Perhaps Dr. Plank should have been a little more thoughtful while playing the game. Regardless, he is stuck. We simply swap two random squares and end the game:


Oh well -- should have done that swap a little sooner....


For this project, we are going to deal with a text-based version of the game. Our programs will have the following parameters:

Dr. Plank has written an interactive game player. Call it as shown below:

UNIX> cd /home/jplank/cs302/Labs/Lab5/bin
UNIX> sb-player
usage: sb-player rows cols min-score-size colors player interactive(y|n) output(y|n) seed
UNIX> sb-player 8 10 5 pbyrg - y y -
Empty Cells: 75     Score: 0

..........
..........
**b....b**
**....b.**
**.g....**
**......**
..........
..g.......

Your Move: 
The format of the board is as follows: When a letter is capitalized, it is on a goal cell. Dots and asterisks stand for empty cells -- asterisks are on the goal cells. If you click on the Print Boards button in the tcl/tk game, it will print out each board on standard output in that format. That's nice for testing.

You can type two commands:

SWAP r1 c1 r2 c2
SCORE r c


In the board above, you can't score anything, so you'll have to swap. We'll swap the blue cell in [2,2] with the green one in [7,2]:

Your Move: SWAP 2 2 7 2

Empty Cells: 70     Score: 0

.r........
..........
**g....b**
**....b.**
**.g....*Y
**......*P
.....rr...
..b.......

Your Move: 


It's incredibly tedious -- play along with us:

Empty Cells: 70     Score: 0

.r........
..........
**g....b**
**....b.**
**.g....*Y
**......*P
.....rr...
..b.......

Your Move: SWAP 0 1 7 2  
Empty Cells: 65     Score: 0

.b........
..........
**g....bB*
**....b.**
P*.g....RY
**......*P
.....rr...
.gry......

Your Move: SWAP 7 3 4 8
Empty Cells: 60     Score: 0

.b.......p
....g.....
**g.p..bB*
**r...b.*R
P*.g....YY
**......*P
.....rr...
.grr......

Your Move: SWAP 3 2 7 1
Empty Cells: 55     Score: 0

.b..r...pp
....g...b.
**g.p..bB*
**g...b.*R
P*.g....YY
**.g....*P
.....rr...
rrrr......

Your Move: SWAP 3 9 0 1
Empty Cells: 50     Score: 0

.r..rgy.pp
....g...b.
**g.p..bB*
**g...b.*B
P*.g....YY
**.g....*P
p...rrr...
rrrr.p....

Your Move: SWAP 6 0 0 1
Empty Cells: 45     Score: 0

.p..rgy.pp
.g..g...b.
**g.p..bB*
**g...b.*B
P*.g..y.YY
**.g..yp*P
r...rrr...
rrrr.py...

Your Move: SWAP 5 9 7 6
Empty Cells: 40     Score: 0

.p..rgy.pp
.g..g...b.
**g.p.pbB*
R*g...by*B
P*.g..y.YY
P*.g..yp*Y
r...rrrb..
rrrr.pp...

Your Move: SWAP 5 0 0 4
Empty Cells: 35     Score: 0

.p..pgy.pp
.g..g.r.b.
G*g.p.pbB*
R*g...by*B
P*.g..y.YY
R*.g..yp*Y
r..grrrb..
rrrrbppy..

Your Move: SWAP 7 4 1 6
Empty Cells: 30     Score: 0

.p..pgy.pp
.g.pg.b.b.
G*g.p.pbB*
R*g.r.by*B
P*pg..y.YY
R*.g.bypBY
r..grrrb..
rrrrrppy..

Your Move: SCORE 5 0
Empty Cells: 37     Score: 50

.p..pgy.pp
.g.pg.b.by
G*g.p.pbB*
R*g.r.byGB
P*pg..y.YY
**.g.bypBY
...g...b..
.p...ppy..

Your Move: 

You'll note, we could have scored cell [5,0] when there were 35 empty cells, but Dr. Plank really wanted to make that patch of red cells bigger.


Program #1: Sb-read

Dr. Plank has provided sb-read.cpp for us. This program takes the four parameters detailed above, reads in a game board with those parameters and prints out some very basic information. For example, the following board:


May be represented by the following text (in input-1.txt):

...yyryy.p
y.rg.yppyp
**gg.yrpPP
GGgbgybp**
R*bg.yrp*P
G*gygyypY*
yyybpby.pb
.pgg.yp.bb


When we run sb-read on it, we get the following:

UNIX> sb-read 8 10 5 pbyrg < input-1.txt
Empty cells:                    20
Non-Empty cells:                60
Number of pieces in goal cells:  8
Sum of their values:            33
UNIX> 
There are three purple pieces in goal cells, one yellow, three green and one red. That makes a total of 3*2 + 4 + 5 + 3*6 = 33.


You should take another look at sb-read.cpp. In particular, look at the Superball class:

class Superball {
  public:
    Superball(int argc, char **argv);
    int r;
    int c;
    int mss;
    int empty;
    vector <int> board;
    vector <int> goals;
    vector <int> colors;
};


Mss is min-score-size. Empty is the number of empty cells in the board. Board is a vector of r * c integers. The element in [i,j] is in entry board[i*c+j], and is either '.', '*' or a lower case letter. goals is another array of r * c integers. It is equal to 0 if the cell is not a goal cell, and 1 if it is a goal cell. Colors is an array of 256 elements, which should be indexed by a letter. Its value is the value of the letter (e.g. in the above example, colors['p'] = 2).

sb-read does all manner of error checking for you. It is a nice program from which to build your other programs.


Program #2: Sb-analyze

You are to write this one.

With sb-analyze, you are to start with sb-read.cpp as a base, and augment it so that it prints out all possible scoring sets. For example, in the above game board (represented by input-1.txt), there are two scoring sets -- the set of 10 purple cells in the upper right-hand corner, and the set of 6 green cells on the left side of the screen. Here is the output to sb_analyze:

UNIX> sb-analyze
usage: sb-analyze rows cols min-score-size colors
UNIX> sb-analyze 8 10 5 pbyrg < input-1.txt
Scoring sets:
  Size: 10  Char: p  Scoring Cell: 2,8
  Size:  6  Char: g  Scoring Cell: 3,0
UNIX> 
Each set must be printed exactly once, but in any order, and with any legal goal cell. Thus, the following output would also be ok:
UNIX> sb-analyze 8 10 5 pbyrg < input-1.txt
Scoring sets:
  Size:  6  Char: g  Scoring Cell: 3,1
  Size: 10  Char: p  Scoring Cell: 2,9
UNIX> 

Think about how you would use the disjoint sets data structure to implement this -- it is a straightforward connected components application. We would recommend augmenting your Superball class with a DisjointSet, and then having a method called analyze_superball() that performs the analysis.

Here's another example:


This is in the file input-2.txt:

yyggyryybp
ggrgpyppyp
RBgggyrpPP
GGgggybpPP
RGygryrpBP
YGyygyypYB
yyybpbyppb
ppggyypbbb

UNIX> sb-analyze 8 10 5 pbyrg < input-2.txt
Scoring sets:
  Size: 14  Char: g  Scoring Cell: 5,1
  Size: 15  Char: p  Scoring Cell: 4,9
  Size:  7  Char: y  Scoring Cell: 5,0
  Size:  5  Char: b  Scoring Cell: 5,9
UNIX> 

Program #3: Sb-play

Your next program takes the same arguments and input as sb-analyze. However, now its job is to print a single move as would be accepted as input for the sb-player program. In other words, it needs to output a SWAP or SCORE line with legal values.


If you have fewer than five pieces and cannot score any, you will lose the game -- you should do that by swapping two legal pieces so that the game can end.

The sb-player program takes as its 5th argument the name of a program that it will use for input. Dr. Plank also also provided three programs - sb-play, sb-play2 and sb-play3 in that directory. sb-play simply swaps two random cells until there are fewer than five empty, then it scores a set if it can. The other two are smarter, but are by no means the best one can do.

Here's sb-player running on sb-play2 (note, sb-player creates a temporary file, so you must run it from your own directory):

UNIX> /home/jplank/cs302/Labs/Lab5/bin/sb-player 8 10 5 pbyrg /home/jplank/cs302/Labs/Lab5/bin/sb-play2 y y -
Empty Cells: 75     Score: 0

g.........
..........
**......**
*Pr.....**
**......**
**..p...**
........b.
..........

Type Return for the next play
It waits for you to press the return key. When you do so, it will send the game board to /home/jplank/cs302/Labs/Lab5/bin/sp-play2 and perform the output. Here's what happens:
Move is: SWAP 5 4 3 2

Empty Cells: 70     Score: 0

g........g
.......y..
**......**
*Pp.....**
**......G*
**..r...**
..g.....b.
........g.

Type Return for the next play
You can bet that the next move will swap that b with one of the g's:
Move is: SWAP 6 8 0 0

Empty Cells: 65     Score: 0

b........g
.......y..
**..b...**
*Pp.g...**
**.....gG*
**..r...**
..g.....g.
.p...p..g.

Type Return for the next play
And so on. If you run it with n for the 6th argument, it will simply run the program without your input:
UNIX> /home/jplank/cs302/Labs/Lab5/bin/sb-player 8 10 5 pbyrg /home/jplank/cs302/Labs/Lab5/bin/sb-play2 n y -
Empty Cells: 75     Score: 0

..........
..........
**......**
**y..y..**
**......**
*P......**
..........
......p.g.

Move is: SWAP 3 5 3 2

... a bunch of output skipped...

Empty Cells:  1     Score: 505

yyrrgggpyy
grrbppg.yg
GYbgygggPB
GBggpgbpPB
PPgggggrYB
YBbybgpbYR
pprrrggggr
byyrppppgg

Move is: SWAP 0 1 7 5

Game over.  Final score = 505
UNIX> 
Even though there were no good moves at the end, the program did a final SWAP so that the game could finish.

If you run with the 7th argument as n, it will only print out the end result, and the last argument can specify a seed (it uses the current time if that argument is "-"), so that you can compare multiple players on the same game:

UNIX> /home/jplank/cs302/Labs/Lab5/bin/sb-player 8 10 5 pbyrg /home/jplank/cs302/Labs/Lab5/bin/sb-play n n 1
Game over.  Final score = 0
UNIX> /home/jplank/cs302/Labs/Lab5/bin/sb-player 8 10 5 pbyrg /home/jplank/cs302/Labs/Lab5/bin/sb-play2 n n 1
Game over.  Final score = 855
UNIX> /home/jplank/cs302/Labs/Lab5/bin/sb-player 8 10 5 pbyrg /home/jplank/cs302/Labs/Lab5/bin/sb-play3 n n 1
Game over.  Final score = 2572
UNIX> 
It can take a while for these to run -- if it appears to be hanging, send the process a QUIT signal and it will print out what the current score is.

Shell Script to Run Multiple Times

The file run_multiple.sh is a shell script to run the player on multiple seeds and average the results. Examples:
UNIX> sh run_multiple.sh 
usage: sh run_multiple.sh r c mss colors player nruns starting_seed
UNIX> sh run_multiple.sh 8 10 5 pbyrg bin/sb-play 10 1
Run   1 - Score:     38  - Average     38.000
Run   2 - Score:      0  - Average     19.000
Run   3 - Score:      0  - Average     12.667
Run   4 - Score:     57  - Average     23.750
Run   5 - Score:      0  - Average     19.000
Run   6 - Score:      0  - Average     15.833
Run   7 - Score:     89  - Average     26.286
Run   8 - Score:     15  - Average     24.875
Run   9 - Score:      0  - Average     22.111
Run  10 - Score:     20  - Average     21.900
UNIX> sh run_multiple.sh 8 10 5 pbyrg bin/sb-play2 10 1
Run   1 - Score:    855  - Average    855.000
Run   2 - Score:    979  - Average    917.000
Run   3 - Score:    650  - Average    828.000
Run   4 - Score:    833  - Average    829.250
Run   5 - Score:    832  - Average    829.800
Run   6 - Score:   3326  - Average   1245.833
Run   7 - Score:   1507  - Average   1283.143
Run   8 - Score:   3643  - Average   1578.125
Run   9 - Score:    610  - Average   1470.556
Run  10 - Score:    862  - Average   1409.700
UNIX> sh run_multiple.sh 8 10 5 pbyrg bin/sb-play3 10 1
Run   1 - Score:   2572  - Average   2572.000
Run   2 - Score:   2708  - Average   2640.000
Run   3 - Score:    745  - Average   2008.333
Run   4 - Score:    424  - Average   1612.250
Run   5 - Score:   1888  - Average   1667.400
Run   6 - Score:   7140  - Average   2579.500
Run   7 - Score:   3475  - Average   2707.429
Run   8 - Score:   1701  - Average   2581.625
Run   9 - Score:   2699  - Average   2594.667
Run  10 - Score:   2291  - Average   2564.300
UNIX> 

Obviously, to get a meaningful average, many more runs (than 10) will be required.


Oh, and make your programs run in reasonable time. Roughly 5 seconds for every thousand points, and if you are burning all that time, your program better be killing Dr. Plank's....


The Superball Challenge

To get credit, your player needs to average over 100 points on runs of 100 games.

We will run a Superball tournament with all of your players with extra lab points going to the winners:

Dr. Plank and I have previously performed the challenge ten times:
Here's the Superball Challenge Hall Of Fame (scores over 500): Here's the Superball Challenge Hall Of Fame (scores over 500):

Rank Average Name Semester
1 31814.13 Grant Bruer CS302, Fall, 2015
2 24278.49 Alexander Teepe CS302, Fall, 2015
3 17367.77 Joseph Connor CS302, Fall, 2014
4 17246.98 Caleb Kornegay CS302, Spring, 2023 (under Dr. Emrich)
5 17021.37 Cory Walker CS302, Fall, 2014
6 16963.40 Seth Kitchens CS302, Fall, 2015
7 14555.83 Ben Arnold CS302, Fall, 2012
8 14555.83 Adam Disney CS302, Fall, 2011
9 13657.79 Isaac Sikkema CS302, Fall, 2018
10 12963.47 Jake Davis CS302, Fall, 2014
11 12634.29 Jake Lamberson CS302, Fall, 2014
12 11722.05 Parker Mitchell CS302, Fall, 2014
13 11670 Colin Smith CS302, Spring, 2023 (under Dr. Emrich)
14 11418.77 James Pickens CS302, Fall, 2014
15 11380.74 Nathan Ziebart CS302, Fall, 2011
16 11291.39 Michael Jugan CS302, Fall, 2010
17 10576.96 Tyler Shields CS302, Fall, 2014
18 10087.23 Maxwell Marcum CS302, Spring, 2022
19 8770.67 Nathan Swartz CS302, Spring, 2019 (Under Dr. Emrich)
20 7607.98 Riley Crockett CS302, Spring, 2022
21 7475.07 Jared Smith CS302, Fall, 2014
22 7216.28 Michael Bowie CS302, Fall, 2018
23 7003.56 Andrew LaPrise CS302, Fall, 2011
24 6100.28 Chris Nagy CS302, Fall, 2015
25 5552.68 Ethan Eisenhauer CS302, Spring, 2023 (under Dr. Emrich)
26 5467.56 Tyler Marshall CS302, Fall, 2013
27 5262.80 Harry Channing CS302, Fall, 2018
28 5116.13 Kyle Bashour CS302, Fall, 2014
29 4808.03 Matt Baumgartner CS302, Fall, 2010
30 4586.51 Jeramy Harrison CS302, Fall, 2013
31 4531.96 Philip Hicks CS302, Spring, 2019 (Under Dr. Emrich)
32 4057.08 Phillip McKnight CS302, Fall, 2015
33 3882.53 Pranshu Bansal CS302, Fall, 2013
34 3882.28 Kemal Fidan CS302, Fall, 2018
35 3852.87 Yaohung Tsai CS302, Fall, 2015
36 3849.24 Chris Richardson CS302, Fall, 2010
37 3809.41 Arthur Vidineyev CS302, Fall, 2015
38 3588.35 Kevin Dunn CS302, Fall, 2014
39 3545.96 Caleb Fisher CS302, Spring, 2022
40 3464.83 Patrick Slavick CS302, Fall, 2012
41 3460.00 Brandan Roachell CS302, Fall, 2020
42 3436.21 sb-play3 CS140, Fall, 2007
43 3400.50 Kody Bloodworth CS302, Fall, 2018
44 3080.15 Andrew Messing CS302, Fall, 2013
45 3059.06 Stephen Qiu CS302, Spring, 2022
46 2903.38 Adam LaClair CS302, Fall, 2013
47 2736.85 Christopher Canaday CS302, Spring, 2022
48 2728.86 Alexander Yu CS302, Spring, 2022
49 2616.00 Rus Refait CS302, Spring, 2020 (Under Dr. Emrich)
50 2555.36 Mohammad Fathi CS302, Fall, 2014
51 2532.89 Trevor Sharpe CS302, Fall, 2015
52 2526.79 Stephen Dao CS302, Fall, 2023 (under Dr. Emrich)
53 2521.44 Justus Camp CS302, Fall, 2018
54 2487.24 Befikir Bogale CS302, Spring, 2022
55 2473.69 Colin Canonaco CS302, Spring, 2022
56 2387.00 Shashank Bandaru CS302, Spring, 2023 (under Dr. Emrich)
57 2354.00 Zach Deguira CS302, Fall, 2020
58 2335.88 Mark Clark CS302, Fall, 2012
59 2307.16 John Burnum CS302, Fall, 2012
60 2205.17 Shawn Cox CS302, Fall, 2011
61 2163.70 Alex Wetherington CS302, Fall, 2011
62 2134.99 Julian Kohann CS302, Fall, 2013
63 2062.54 Gitasuk Jur CS302, Spring, 2022
64 2011.38 Wells Phillip CS302, Fall, 2015
65 1919.72 Ravi Patel CS302, Spring, 2019 (Under Dr. Emrich)
66 1854.00 Sam Aba CS302, Spring, 2020 (Under Dr. Emrich)
67 1849.73 Andrew Lay CS302, Spring, 2022
68 1828.05 Fatima Bowers CS302, Spring, 2022
69 1778.83 Keith Clinart CS302, Fall, 2011
70 1740.19 Luke Bechtel CS302, Fall, 2014
71 1635.30 Justin Bowers CS302, Spring, 2022
72 1634.49 William Brummette CS302, Fall, 2013
73 1602.83 Forrest Sable CS302, Fall, 2014
74 1498.87 Tom Hills CS302, Spring, 2019 (Under Dr. Emrich)
75 1470.84 Christopher Tester CS302, Fall, 2014
76 1446.00 Noah Burgin CS302, Spring, 2020 (Under Dr. Emrich)
77 1433.48 Xiao Zhou CS302, Fall, 2015
78 1430.54 Jonathan Burns CS302, Fall, 2018
79 1399.08 Meghan Brandt CS302, Spring, 2022
80 1340.32 John Murray CS302, Fall, 2012
81 1329.34 Benjamin Brock CS302, Fall, 2013
82 1301.00 Henry Brand CS302, Spring, 2022
83 1292.5 Jason Choi CS302, Spring, 2023 (under Dr. Emrich)
84 1259.00 John Heath CS302, Spring, 2023 (under Dr. Emrich)
85 1257.56 Dylan Lee CS302, Fall, 2018
86 1202.06 Bandara CS302, Fall, 2014
87 1149.80 Will Houston CS302, Fall, 2010
88 1148.99 Fort Hunter CS302, Spring, 2022
82 1119.85 Kevin Chiang CS302, Fall, 2014
83 1096.48 Daniel Cash CS302, Fall, 2011
84 1076.30 Abrian Abir CS302, Spring, 2022
85 1059.91 Kaleb McClure CS302, Fall, 2013
86 1058.26 sb-play2 CS140, Fall, 2007
87 1029.63 Lydia San George CS302, Fall, 2018
88 1020.55 Jihun Kim CS302, Spring, 2022
89 1019.72 Justin Langston CS302, Spring, 2019 (Under Dr. Emrich)
90 1016.15 Harrison Hoytt CS302, Spring, 2022
91 972.36 Erik Rutledge CS302, Fall, 2013
92 959.79 Daniel Nichols CS302, Fall, 2018
93 917.92 Vasu Kalaria CS302, Fall, 2015
94 916.54 Cody Blankenship CS302, Spring, 2022
95 909.35 Andrew Mueller CS360?, Spring, 2022
96 908.09 Chris Rains CS302, Fall, 2012
97 875.44 Allen McBride CS302, Fall, 2012
98 856.00 Tim Krenz CS302, Spring, 2019 (Under Dr. Emrich)
99 852.32 Jonathan Graham CS302, Spring, 2022
100 843.69 Jacob Looney CS302, Spring, 2022
101 840.94 Spencer Howell CS302, Fall, 2018
102 831.74 Ethan Kessinger CS302, Spring, 2022
103 830.79 David Cunningham CS302, Fall, 2014
104 826.00 Kincaid Mcgee CS302, Fall, 2020
105 817.17 Alex Nguyen CS302, Spring, 2022
106 810.17 Collin Bell CS302, Fall, 2012
107 763.58 Jacob Lambert CS302, Fall, 2013
108 707.16 Shivam Mistry CS302, Spring, 2022
109 703.71 Shanna Wallace CS302, Spring, 2022
110 703.67 Scott Marcus CS302, Fall, 2015
111 703.00 Don Lopez CS140, Fall, 2007
112 700.90 Tony Abston CS302, Fall, 2015
113 682.56 Jackson Collier CS302, Fall, 2014
114 681.00 Holland Johnson CS302, Fall, 2020
115 677.83 KC Bentjen CS302, Fall, 2011
116 677.74 Andrew Artates CS302, Spring, 2019 (Under Dr. Emrich)
117 665.60 Joshua Clark CS302, Fall, 2012
118 659.96 Warren Dewit CS302, Fall, 2010
119 654.67 Coburn Brandon CS302, Fall, 2015
120 650.98 Joaquin Bujalance CS140, Fall, 2007
121 643.13 John Blackaby CS302, Spring, 2022
122 638.14 Justin Mcknight CS302, Spring, 2022
123 630.73 Dylan Devries CS302, Fall, 2018
124 630.10 Winston Boyd CS302, Fall, 2018
125 626.62 Elliot Greenlee CS302, Fall, 2014
126 616.00 Joseph Wehby CS302, Spring, 2022
127 609.58 Ethan Maness CS302, Spring, 2022
128 603.14 Joseph Eaton CS302, Spring, 2022
129 594.02 James Tucker CS302, Fall, 2015
130 586.71 Jonathan Ting CS302, Spring, 2019 (Under Dr. Emrich)
131 586.56 Andrew Berger CS302, Spring, 2019 (Under Dr. Emrich)
132 581.29 Mason Stott CS302, Spring, 2022
133 571.02 Rocco Febbo CS302, Fall, 2018
134 558.62 Matthew Bowlby CS302, Spring, 2022
135 557.01 Eli Kell CS302, Spring, 2022
136 555.40 Jovan Yoshioka CS302, Spring, 2022
137 554.94 Jared Burris CS302, Fall, 2015
138 546.52 Andrew Rutter CS302, Spring, 2022
139 539.00 Braden Butler CS302, Fall, 2020
140 534.24 Christian Graham CS302, Spring, 2022
141 524.21 Reagan Austin CS302, Spring, 2022
142 508.04 Victoria Florence CS302, Fall, 2015


Hints

Play the game for a bit to try to figure out some strategies. However, one good way to write a game player is to figure out a way to come up with a rating for a game board. Then when you are faced with making a move, you analyze all potential moves by trying them out and choosing the one that gives you the resulting board with the highest rating.