# Iterated Prisoner's Trilemma

CHALLENGE STATUS: OPEN

Comment, open a PR, or otherwise yell at me if I'm missing your bot.

Prisoner's dilemma ... with three choices. Crazy, huh?

Here's our payoff matrix. Player A on the left, B on the top

A,B| C | N | D
---|---|---|---
C |3,3|4,1|0,5
N |1,4|2,2|3,2
D |5,0|2,3|1,1


The payoff matrix is engineered so that it's best for both players to always cooperate, but you can gain (usually) by choosing Neutral or Defection.

Here's some (competing) example bots.

# turns out if you don't actually have to implement __init__(). TIL!

class AllC:
def round(self, _): return "C"
class AllN:
def round(self, _): return "N"
class AllD:
def round(self, _): return "D"
class RandomBot:
def round(self, _): return random.choice(["C", "N", "D"])

# Actually using an identically-behaving "FastGrudger".
class Grudger:
def __init__(self):
self.history = []
def round(self, last):
if(last):
self.history.append(last)
if(self.history.count("D") > 0):
return "D"
return "C"

class TitForTat:
def round(self, last):
if(last == "D"):
return "D"
return "C"


Your bot is a Python3 class. A new instance is created for every game, and round() is called each round, with your opponent's choice from last round (or None, if it's the first round)

There's a 50 rep bounty for the winner in like a month.

## Specifics

• Every bot plays every other bot (1v1), including itself, in [REDACTED] rounds.
• Standard loopholes disallowed.
• No messing with anything outside your class or other underhanded shenanigains.
• You may submit up to five bots.
• Yes, you can implement handshake.
• Any response other than C, N, or D will be silently taken as N.
• Each bot's points from every game they play will be totaled up and compared.

Check!

## Other Languages

I'll throw together an API if anyone needs it.

## Scores: 2018-11-27

27 bots, 729 games.

name            | avg. score/round
----------------|-------------------
PatternFinder   | 3.152
DirichletDice2  | 3.019
EvaluaterBot    | 2.971
Ensemble        | 2.800
DirichletDice   | 2.763
Shifting        | 2.737
FastGrudger     | 2.632
Nash2           | 2.574
HistoricAverage | 2.552
LastOptimalBot  | 2.532
Number6         | 2.531
HandshakeBot    | 2.458
OldTitForTat    | 2.411
WeightedAverage | 2.403
TitForTat       | 2.328
AllD            | 2.272
Tetragram       | 2.256
Nash            | 2.193
Useless         | 2.140
RandomBot       | 2.018
CopyCat         | 1.902
TatForTit       | 1.891
NeverCOOP       | 1.710
AllC            | 1.565
AllN            | 1.446
Kevin           | 1.322

• How are bots put against each other? I get from the Grudger that there are always two bots against/with each other and the enemy's last choice is passed to the bot. How many rounds are played? And for a game: Does only the result count (i.e. who won) or also the points? – Black Owl Kai Nov 12 '18 at 17:52
• You would get more entries if you made this language-agnostic, or at least broader. You could have a wrapper python class that spawns a process and sends it text commands to get back text responses. – Sparr Nov 12 '18 at 17:54
• Done. This was on the sandbox for like a month! – SIGSTACKFAULT Nov 12 '18 at 17:56
• If you wrap most of main.py in while len(botlist) > 1: with botlist.remove(lowest_scoring_bot) at the bottom of the loop, you get an elimination tournament with interesting results. – Sparr Nov 13 '18 at 7:39
• Another version of this someday might pass the entire interaction history rather than just the last move. It doesn't change much although it simplifies user code slightly. But it would allow for extensions, such as noisy communication channels that clarify over time: "Really, a D, even though I've said C four times in a row? No, I didn't say D; what do you take me for? Oh, sorry, can we just forget that round?" – Scott Sauyet Nov 13 '18 at 13:31

# EvaluaterBot

class EvaluaterBot:
def __init__(self):
self.c2i = {"C":0, "N":1, "D":2}
self.i2c = {0:"C", 1:"N", 2:"D"}
self.history = [[0, 0, 0], [0, 0, 0], [0, 0, 0]]
self.last = [None, None]

def round(self, last):
if self.last[0] == None:
ret = 2
else:
# Input the latest enemy action (the reaction to my action 2 rounds ago)
# into the history
self.history[self.last[0]][self.c2i[last]] += 1
# The enemy will react to the last action I did
prediction,_ = max(enumerate(self.history[self.last[1]]), key=lambda l:l[1])
ret = (prediction - 1) % 3
self.last = [self.last[1], ret]
return self.i2c[ret]


Wins against all previously submitted bots except (maybe) the random bot (but it could have an advantage, because it picks D in a draw and D should optimal) and plays a constant draw against themself.

• Yep, beats everything. – SIGSTACKFAULT Nov 12 '18 at 19:43
• Scratch that, PatternFinder beats it by a bit. – SIGSTACKFAULT Nov 13 '18 at 14:25

# TatForTit

class TatForTit:
def round(self, last):
if(last == "C"):
return "N"
return "D"


This bot will alternate picking D N D N while TitForTat alternates C D C D, for an average net gain of 3 points per round if I have read the payout matrix correctly. I think this might be optimal against TitForTat. Obviously it could be improved to detect a non-TFT opponent and adopt other strategies, but I was just aiming for the original bounty.

# NashEquilibrium

This bot has taken a game theory class in college but was lazy and didn't go to the class where they covered iterated games. So he only plays single game mixed nash equilibrium. Turns out 1/5 2/5 2/5 is the mixed NE for the payoffs.

class NashEquilibrium:
def round(self, _):
a = random.random()
if a <= 0.2:
return "C"
elif a <= 0.6:
return "N"
else:
return "D"


Constant Abusing Nash Equilibrium

This bot picked up a lesson or two from his lazy brother. His lazy brother's problem was that he didn't take advantage of fixed strategies. This version checks if the opponent is a constant player or titfortat and plays accordingly, else it plays the regular nash equilibrium.

It's only downside is that it averages 2.2 points per turn playing against itself.

class NashEquilibrium2:

def __init__(self):
self.opphistory = [None, None, None]
self.titfortatcounter = 0
self.titfortatflag = 0
self.mylast = "C"
self.constantflag = 0
self.myret = "C"

def round(self, last):
self.opphistory.pop(0)
self.opphistory.append(last)

# check if its a constant bot, if so exploit
if self.opphistory.count(self.opphistory[0]) == 3:
self.constantflag = 1
if last == "C":
self.myret = "D"
elif last == "N":
self.myret = "C"
elif last == "D":
self.myret = "N"

# check if its a titfortat bot, if so exploit
# give it 2 chances to see if its titfortat as it might happen randomly
if self.mylast == "D" and last == "D":
self.titfortatcounter = self.titfortatcounter + 1

if self.mylast == "D" and last!= "D":
self.titfortatcounter = 0

if self.titfortatcounter >= 3:
self.titfortatflag = 1

if self.titfortatflag == 1:
if last == "C":
self.myret = "D"
elif last == "D":
self.myret = "N"
elif last == "N":
# tit for tat doesn't return N, we made a mistake somewhere
self.titfortatflag = 0
self.titfortatcounter = 0

# else play the single game nash equilibrium
if self.constantflag == 0 and self.titfortatflag == 0:
a = random.random()
if a <= 0.2:
self.myret = "C"
elif a <= 0.6:
self.myret = "N"
else:
self.myret = "D"

self.mylast = self.myret
return self.myret

• NashEquilibrium.round needs to take arguments even if it doesn't use them, so as to fit the expected function prototype. – Ray Nov 13 '18 at 3:18
• Thank you fixed it – Ofya Nov 13 '18 at 3:45
• Little shorter: class NashEquilibrium: def round(self, _): a = random.random() for k, v in [(0.2, "C"), (0.6, "N"), (1, "D")]: if a <= k: return v – Rob Grant Nov 14 '18 at 1:01

# PatternFinder

class PatternFinder:
def __init__(self):
import collections
self.size = 10
self.moves = [None]
self.other = []
self.patterns = collections.defaultdict(list)
self.counter_moves = {"C":"D", "N":"C", "D":"N"}
self.initial_move = "D"
self.pattern_length_exponent = 1
self.pattern_age_exponent = 1
self.debug = False
def round(self, last):
self.other.append(last)
best_pattern_match = None
best_pattern_score = None
best_pattern_response = None
self.debug and print("match so far:",tuple(zip(self.moves,self.other)))
for turn in range(max(0,len(self.moves)-self.size),len(self.moves)):
# record patterns ending with the move that just happened
pattern_full = tuple(zip(self.moves[turn:],self.other[turn:]))
if len(pattern_full) > 1:
pattern_trunc = pattern_full[:-1]
pattern_trunc_result = pattern_full[-1][1]
self.patterns[pattern_trunc].append([pattern_trunc_result,len(self.moves)-1])
if pattern_full in self.patterns:
# we've seen this pattern at least once before
self.debug and print("I've seen",pattern_full,"before:",self.patterns[pattern_full])
for [response,turn_num] in self.patterns[pattern_full]:
score = len(pattern_full) ** self.pattern_length_exponent / (len(self.moves) - turn_num) ** self.pattern_age_exponent
if best_pattern_score == None or score > best_pattern_score:
best_pattern_match = pattern_full
best_pattern_score = score
best_pattern_response = response
# this could be much smarter about aggregating previous responses
if best_pattern_response:
move = self.counter_moves[best_pattern_response]
else:
# fall back to playing nice
move = "C"
self.moves.append(move)
self.debug and print("I choose",move)
return move


This bot looks for previous occurrences of the recent game state to see how the opponent responded to those occurrences, with a preference for longer pattern matches and more recent matches, then plays the move that will "beat" the opponent's predicted move. There's a lot of room for it to be smarter with all the data it keeps track of, but I ran out of time to work on it.

• When you get the time, mind giving her an optimization pass? It's easily the largest time-sink. – SIGSTACKFAULT Nov 13 '18 at 3:46
• @Blacksilver I just reduced the maximum pattern length from 100 to 10. It should run almost instantly now if you're running <200 rounds – Sparr Nov 13 '18 at 5:55
• Maybe using a highly composite number (i.e., 12) would score better? – SIGSTACKFAULT Nov 13 '18 at 16:09

class Jade:
def __init__(self):
self.dRate = 0.001
self.nRate = 0.003

def round(self, last):
if last == 'D':
self.dRate *= 1.1
self.nRate *= 1.2
elif last == 'N':
self.dRate *= 1.03
self.nRate *= 1.05
self.dRate = min(self.dRate, 1)
self.nRate = min(self.nRate, 1)

x = random.random()
if x > (1 - self.dRate):
return 'D'
elif x > (1 - self.nRate):
return 'N'
else:
return 'C'


Starts out optimistic, but gets progressively more bitter as the opponent refuses to cooperate. Lots of magic constants that could probably be tweaked, but this probably isn't going to do well enough to justify the time.

# Ensemble

This runs an ensemble of related models. The individual models consider different amounts of history, and have the option of either always choosing the move that will optimize the expected payout difference, or will randomly select a move in proportion to expected payout difference.

Each member of the ensemble then votes on their preferred move. They get a number of votes equal to how much more they've won than the opponent (which means that terrible models will get negative votes). Whichever move wins the vote is then selected.

(They should probably split their votes among the moves in proportion to how much they favor each, but I don't care enough to do that right now.)

It beats everything posted so far except EvaluaterBot and PatternFinder. (One-on-one, it beats EvaluaterBot and loses to PatternFinder).

from collections import defaultdict
import random
class Number6:
class Choices:
def __init__(self, C = 0, N = 0, D = 0):
self.C = C
self.N = N
self.D = D

def __init__(self, strategy = "maxExpected", markov_order = 3):
self.MARKOV_ORDER = markov_order;
self.my_choices = ""
self.opponent = defaultdict(lambda: self.Choices())
self.choice = None # previous choice
self.payoff = {
"C": { "C": 3-3, "N": 4-1, "D": 0-5 },
"N": { "C": 1-4, "N": 2-2, "D": 3-2 },
"D": { "C": 5-0, "N": 2-3, "D": 1-1 },
}
self.total_payoff = 0

# if random, will choose in proportion to payoff.
# otherwise, will always choose argmax
self.strategy = strategy
# maxExpected: maximize expected relative payoff
# random: like maxExpected, but it chooses in proportion to E[payoff]
# argmax: always choose the option that is optimal for expected opponent choice

def update_opponent_model(self, last):
for i in range(0, self.MARKOV_ORDER):
hist = self.my_choices[i:]
self.opponent[hist].C += ("C" == last)
self.opponent[hist].N += ("N" == last)
self.opponent[hist].D += ("D" == last)

def normalize(self, counts):
sum = float(counts.C + counts.N + counts.D)
if 0 == sum:
return self.Choices(1.0 / 3.0, 1.0 / 3.0, 1.0 / 3.0)
return self.Choices(
counts.C / sum, counts.N / sum, counts.D / sum)

def get_distribution(self):
for i in range(0, self.MARKOV_ORDER):
hist = self.my_choices[i:]
#print "check hist = " + hist
if hist in self.opponent:
return self.normalize(self.opponent[hist])

return self.Choices(1.0 / 3.0, 1.0 / 3.0, 1.0 / 3.0)

def choose(self, dist):
payoff = self.Choices()
# We're interested in *beating the opponent*, not
# maximizing our score, so we optimize the difference
payoff.C = (3-3) * dist.C + (4-1) * dist.N + (0-5) * dist.D
payoff.N = (1-4) * dist.C + (2-2) * dist.N + (3-2) * dist.D
payoff.D = (5-0) * dist.C + (2-3) * dist.N + (1-1) * dist.D

# D has slightly better payoff on uniform opponent,
# so we select it on ties
if self.strategy == "maxExpected":
if payoff.C > payoff.N:
return "C" if payoff.C > payoff.D else "D"
return "N" if payoff.N > payoff.D else "D"
elif self.strategy == "randomize":
payoff = self.normalize(payoff)
r = random.uniform(0.0, 1.0)
if (r < payoff.C): return "C"
return "N" if (r < payoff.N) else "D"
elif self.strategy == "argMax":
if dist.C > dist.N:
return "D" if dist.C > dist.D else "N"
return "C" if dist.N > dist.D else "N"

assert(0) #, "I am not a number! I am a free man!")

def update_history(self):
self.my_choices += self.choice
if len(self.my_choices) > self.MARKOV_ORDER:
assert(len(self.my_choices) == self.MARKOV_ORDER + 1)
self.my_choices = self.my_choices[1:]

def round(self, last):
if last: self.update_opponent_model(last)

dist = self.get_distribution()
self.choice = self.choose(dist)
self.update_history()
return self.choice

class Ensemble:
def __init__(self):
self.models = []
self.prev_choice = []
for order in range(0, 6):
self.models.append(Number6("maxExpected", order))
self.models.append(Number6("randomize", order))
#self.models.append(Number6("argMax", order))
for i in range(0, len(self.models)):
self.prev_choice.append("D")

self.payoff = {
"C": { "C": 3-3, "N": 4-1, "D": 0-5 },
"N": { "C": 1-4, "N": 2-2, "D": 3-2 },
"D": { "C": 5-0, "N": 2-3, "D": 1-1 },
}

def round(self, last):
if last:
for i in range(0, len(self.models)):

# vote. Sufficiently terrible models get negative votes
C = 0
N = 0
D = 0
for i in range(0, len(self.models)):
choice = self.models[i].round(last)
if "C" == choice: C += self.votes[i]
if "N" == choice: N += self.votes[i]
if "D" == choice: D += self.votes[i]
self.prev_choice[i] = choice

if C > D and C > N: return "C"
elif N > D: return "N"
else: return "D"


# Test Framework

In case anyone else finds it useful, here's a test framework for looking at individual matchups. Python2. Just put all the opponents you're interested in in opponents.py, and change the references to Ensemble to your own.

import sys, inspect
import opponents
from ensemble import Ensemble

def count_payoff(label, them):
if None == them: return
me = choices[label]
payoff = {
"C": { "C": 3-3, "N": 4-1, "D": 0-5 },
"N": { "C": 1-4, "N": 2-2, "D": 3-2 },
"D": { "C": 5-0, "N": 2-3, "D": 1-1 },
}
if label not in total_payoff: total_payoff[label] = 0
total_payoff[label] += payoff[me][them]

def update_hist(label, choice):
choices[label] = choice

opponents = [ x[1] for x
in inspect.getmembers(sys.modules['opponents'], inspect.isclass)]

for k in opponents:
total_payoff = {}

for j in range(0, 100):
A = Ensemble()
B = k()
choices = {}

aChoice = None
bChoice = None
for i in range(0, 100):
count_payoff(A.__class__.__name__, bChoice)
a = A.round(bChoice)
update_hist(A.__class__.__name__, a)

count_payoff(B.__class__.__name__, aChoice)
b = B.round(aChoice)
update_hist(B.__class__.__name__, b)

aChoice = a
bChoice = b
print total_payoff

• The controller is ready, you didn't have to do all that... – SIGSTACKFAULT Nov 13 '18 at 3:40
• @Blacksilver I realized that just as I was about to submit. But this one works in versions before 3.6, and gives information about individual matchups that can help identify weak spots, so it wasn't a complete waste of time. – Ray Nov 13 '18 at 3:42
• Fair enough; running now. I'll probably add options to my controller to do similar things. – SIGSTACKFAULT Nov 13 '18 at 3:45
• "It beats everything posted so far except Ensemble and PatternFinder" I'm honored :) – Sparr Nov 13 '18 at 7:19
• @Sparr Oops. That was supposed to say EvaluaterBot and PatternFinder. But that's when comparing total score against the entire field. PatternFinder remains the only one that beats this in a direct match up. – Ray Nov 13 '18 at 8:04

## OldTitForTat

Old school player is too lazy to update for the new rules.

class OldTitForTat:
def round(self, last):
if(last == None)
return "C"
if(last == "C"):
return "C"
return "D"


# NeverCOOP

class NeverCOOP:
def round(self, last):
try:
if last in "ND":
return "D"
else:
return "N"
except:
return "N"


If the opposing bot defects or is neutral, choose defect. Otherwise if this is the first turn or the opposing bot cooperates, choose neutral. I'm not sure how good this will work...

• What's the try/except for? – SIGSTACKFAULT Nov 12 '18 at 19:09
• @Blacksilver I'd assume it serves the same purpose as the if(last): in your Grudger bot, detecting whether there was a previous round. – ETHproductions Nov 12 '18 at 19:31
• ahh, I see. None in "ND" errors. – SIGSTACKFAULT Nov 12 '18 at 19:38
• Because if last and last in "ND": was too complicated? – user253751 Nov 12 '18 at 21:29

# LastOptimalBot

class LastOptimalBot:
def round(self, last):
return "N" if last == "D" else ("D" if last == "C" else "C")


Assumes that the opposing bot will always play the same move again, and chooses the one that has the best payoff against it.

Averages:

Me   Opp
2.6  2    vs TitForTat
5    0    vs AllC
4    1    vs AllN
3    2    vs AllD
3.5  3.5  vs Random
3    2    vs Grudger
2    2    vs LastOptimalBot
1    3.5  vs TatForTit
4    1    vs NeverCOOP
1    4    vs EvaluaterBot
2.28 2.24 vs NashEquilibrium

2.91 average overall

• oof. Maybe T4T would do better as return last. – SIGSTACKFAULT Nov 12 '18 at 18:11
• I'd like that! If TitForTat were return last, LOB would go 18-9 over 6 rounds rather than the 13-10 over 5 rounds it's currently getting. I think it's fine as is - don't worry about optimizing the example bots. – Spitemaster Nov 12 '18 at 18:30
• return last would be a better T4T for this challenge, I think – Sparr Nov 12 '18 at 18:43
• Just tried -- the if(last): return last; else: return "C" is worse. – SIGSTACKFAULT Nov 12 '18 at 18:59
• Right, but as @Sparr was saying, it might be more appropriate. Up to you, I suppose. – Spitemaster Nov 12 '18 at 19:00

# CopyCat

class CopyCat:
def round(self, last):
if last:
return last
return "C"


Copies the opponent's last move.
I don't expect this to do well, but no one had implemented this classic yet.

# Handshake

class HandshakeBot:
def __init__(self):
self.handshake_length = 4
self.handshake = ["N","N","C","D"]
while len(self.handshake) < self.handshake_length:
self.handshake *= 2
self.handshake = self.handshake[:self.handshake_length]
self.opp_hand = []
self.friendly = None
def round(self, last):
if last:
if self.friendly == None:
# still trying to handshake
self.opp_hand.append(last)
if self.opp_hand[-1] != self.handshake[len(self.opp_hand)-1]:
self.friendly = False
return "D"
if len(self.opp_hand) == len(self.handshake):
self.friendly = True
return "C"
return self.handshake[len(self.opp_hand)]
elif self.friendly == True:
# successful handshake and continued cooperation
if last == "C":
return "C"
self.friendly = False
return "D"
else:
# failed handshake or abandoned cooperation
return "N" if last == "D" else ("D" if last == "C" else "C")
return self.handshake[0]


Recognizes when it's playing against itself, then cooperates. Otherwise mimics LastOptimalBot which seems like the best one-line strategy. Performs worse than LastOptimalBot, by an amount inversely proportional to the number of rounds. Obviously would do better if there were more copies of it in the field *cough**wink*.

• Just submit a few clones that have different non-handshake behavior. – SIGSTACKFAULT Nov 13 '18 at 22:52
• That seems exploit-y. I could submit one such clone for every simple behavior represented here. – Sparr Nov 14 '18 at 0:12
• I've added an extra clause saying that you can only submit max five bots. – SIGSTACKFAULT Nov 14 '18 at 2:14

# Improved Dirichlet Dice

import random

class DirichletDice2:
def __init__(self):

self.alpha = dict(
C = {'C' : 1, 'N' : 1, 'D' : 1},
N = {'C' : 1, 'N' : 1, 'D' : 1},
D = {'C' : 1, 'N' : 1, 'D' : 1}
)
self.myLast = [None, None]
self.payoff = dict(
C = { "C": 0, "N": 3, "D": -5 },
N = { "C": -3, "N": 0, "D": 1 },
D = { "C": 5, "N": -1, "D": 0 }
)

def DirichletDraw(self, key):
alpha = self.alpha[key].values()
mu = [random.gammavariate(a,1) for a in alpha]
mu = [m / sum(mu) for m in mu]
return mu

def ExpectedPayoff(self, probs):
expectedPayoff = {}
for val in ['C','N','D']:
payoff = sum([p * v for p,v in zip(probs, self.payoff[val].values())])
expectedPayoff[val] = payoff
return expectedPayoff

def round(self, last):
if last is None:
self.myLast[0] = 'D'
return 'D'

#update dice corresponding to opponent's last response to my
#outcome two turns ago
if self.myLast[1] is not None:
self.alpha[self.myLast[1]][last] += 1

#draw probs for my opponent's roll from Dirichlet distribution and then return the optimal response
mu = self.DirichletDraw(self.myLast[0])
expectedPayoff = self.ExpectedPayoff(mu)
res = max(expectedPayoff, key=expectedPayoff.get)

#update myLast
self.myLast[1] = self.myLast[0]
self.myLast[0] = res

return res


This is an improved version of Dirichlet Dice. Instead of taking the expected multinomial distribution from the Dirichlet distribution, it draws a Multinomial distribution randomly from the Dirichlet distribution. Then, instead of drawing from the Multinomial and giving the optimal response to that, it gives the optimal expected response to the given Multinomial using the points. So the randomness has essentially been shifted from the Multinomial draw to the Dirichlet draw. Also, the priors are more flat now, to encourage exploration.

It's "improved" because it now accounts for the points system by giving the best expected value against the probabilities, while maintaining its randomness by drawing the probabilities themselves. Before, I tried simply doing the best expected payoff from the expected probabilities, but that did badly because it just got stuck, and didn't explore enough to update its dice. Also it was more predictable and exploitable.

Original submission:

# Dirichlet Dice

import random

class DirichletDice:
def __init__(self):

self.alpha = dict(
C = {'C' : 2, 'N' : 3, 'D' : 1},
N = {'C' : 1, 'N' : 2, 'D' : 3},
D = {'C' : 3, 'N' : 1, 'D' : 2}
)

self.Response = {'C' : 'D', 'N' : 'C', 'D' : 'N'}
self.myLast = [None, None]

#expected value of the dirichlet distribution given by Alpha
def MultinomialDraw(self, key):
alpha = list(self.alpha[key].values())
probs = [x / sum(alpha) for x in alpha]
outcome = random.choices(['C','N','D'], weights=probs)[0]
return outcome

def round(self, last):
if last is None:
self.myLast[0] = 'D'
return 'D'

#update dice corresponding to opponent's last response to my
#outcome two turns ago
if self.myLast[1] is not None:
self.alpha[self.myLast[1]][last] += 1

#predict opponent's move based on my last move
predict = self.MultinomialDraw(self.myLast[0])
res = self.Response[predict]

#update myLast
self.myLast[1] = self.myLast[0]
self.myLast[0] = res

return res


Basically I'm assuming that the opponent's response to my last output is a multinomial variable (weighted dice), one for each of my outputs, so there's a dice for "C", one for "N", and one for "D". So if my last roll was, for example, a "N" then I roll the "N-dice" to guess what their response would be to my "N". I begin with a Dirichlet prior that assumes that my opponent is somewhat "smart" (more likely to play the one with the best payoff against my last roll, least likely to play the one with the worst payoff). I generate the "expected" Multinomial distribution from the appropriate Dirichlet prior (this is the expected value of the probability distribution over their dice weights). I roll the weighted dice of my last output, and respond with the one with the best payoff against that dice outcome.

Starting in the third round, I do a Bayesian update of the appropriate Dirichlet prior of my opponent's last response to the thing I played two rounds ago. I'm trying to iteratively learn their dice weightings.

I could have also simply picked the response with the best "expected" outcome once generating the dice, instead of simply rolling the dice and responding to the outcome. However, I wanted to keep the randomness in, so that my bot is less vulnerable to the ones that try to predict a pattern.

# Kevin

class Kevin:
def round(self, last):
return {"C":"N","N":"D","D":"C",None:"N"} [last]


Picks the worst choice. The worst bot made.

# Useless

import random

class Useless:
def __init__(self):
self.lastLast = None

def round(self, last):
tempLastLast = self.lastLast
self.lastLast = last

if(last == "D" and tempLastLast == "N"):
return "C"
if(last == "D" and tempLastLast == "C"):
return "N"

if(last == "N" and tempLastLast == "D"):
return "C"
if(last == "N" and tempLastLast == "C"):
return "D"

if(last == "C" and tempLastLast == "D"):
return "N"
if(last == "C" and tempLastLast == "N"):
return "D"

return random.choice("CND")


It looks at the last two moves done by the opponent and picks the most not done else it picks something random. There is probably a better way of doing this.

# Historic Average

class HistoricAverage:
PAYOFFS = {
"C":{"C":3,"N":1,"D":5},
"N":{"C":4,"N":2,"D":2},
"D":{"C":0,"N":3,"D":1}}
def __init__(self):
self.payoffsum = {"C":0, "N":0, "D":0}
def round(this, last):
if(last != None):
for x in this.payoffsum:
this.payoffsum[x] += HistoricAverage.PAYOFFS[last][x]
return max(this.payoffsum, key=this.payoffsum.get)


Looks at history and finds the action that would have been best on average. Starts cooperative.

• This could run faster if it didn't re-calculate the averages every round. – Sparr Nov 13 '18 at 19:57
• @Sparr true. I edited it so it does now. – MegaTom Nov 14 '18 at 20:19

# Weighted Average

class WeightedAverageBot:
def __init__(self):
self.C_bias = 1/4
self.N = self.C_bias
self.D = self.C_bias
self.prev_weight = 1/2
def round(self, last):
if last:
if last == "C" or last == "N":
self.D *= self.prev_weight
if last == "C" or last == "D":
self.N *= self.prev_weight
if last == "N":
self.N = 1 - ((1 - self.N) * self.prev_weight)
if last == "D":
self.D = 1 - ((1 - self.D) * self.prev_weight)
if self.N <= self.C_bias and self.D <= self.C_bias:
return "D"
if self.N > self.D:
return "C"
return "N"


The opponent's behavior is modeled as a right triangle with corners for C N D at 0,0 0,1 1,0 respectively. Each opponent move shifts the point within that triangle toward that corner, and we play to beat the move indicated by the point (with C being given an adjustably small slice of the triangle). In theory I wanted this to have a longer memory with more weight to previous moves, but in practice the current meta favors bots that change quickly, so this devolves into an approximation of LastOptimalBot against most enemies. Posting for posterity; maybe someone will be inspired.

# Tetragram

import itertools

class Tetragram:
def __init__(self):
self.history = {x: ['C'] for x in itertools.product('CND', repeat=4)}
self.theirs = []
self.previous = None

def round(self, last):
if self.previous is not None and len(self.previous) == 4:
self.history[self.previous].append(last)
if last is not None:
self.theirs = (self.theirs + [last])[-3:]

if self.previous is not None and len(self.previous) == 4:
expected = random.choice(self.history[self.previous])
if expected == 'C':
choice = 'C'
elif expected == 'N':
choice = 'C'
else:
choice = 'N'
else:
choice = 'C'

self.previous = tuple(self.theirs + [choice])
return choice


Try to find a pattern in the opponent's moves, assuming they're also watching our last move.

# ShiftingOptimalBot

class ShiftingOptimalBot:
def __init__(self):
# wins, draws, losses
self.history = [0,0,0]
self.lastMove = None
self.state = 0
def round(self, last):
if last == None:
self.lastMove = "C"
return self.lastMove
if last == self.lastMove:
self.history[1] += 1
elif (last == "C" and self.lastMove == "D") or (last == "D" and self.lastMove == "N") or (last == "N" and self.lastMove == "C"):
self.history[0] += 1
else:
self.history[2] += 1

if self.history[0] + 1 < self.history[2] or self.history[2] > 5:
self.state = (self.state + 1) % 3
self.history = [0,0,0]
if self.history[1] > self.history[0] + self.history[2] + 2:
self.state = (self.state + 2) % 3
self.history = [0,0,0]

if self.state == 0:
self.lastMove = "N" if last == "D" else ("D" if last == "C" else "C")
elif self.state == 1:
self.lastMove = last
else:
self.lastMove = "C" if last == "D" else ("N" if last == "C" else "D")
return self.lastMove


This bot uses LastOptimalBot's algorithm as long as it's winning. If the other bot starts predicting it, however, it will start playing whichever move its opponent played last (which is the move that beats the move that would beat LastOptimalBot). It cycles through simple transpositions of those algorithms as long as it continues to lose (or when it gets bored by drawing a lot).

Honestly, I'm surprised that LastOptimalBot is sitting in 5th as I post this. I'm fairly certain this will do better, assuming that I wrote this python correctly.

# HandshakePatternMatch

from .patternfinder import PatternFinder
import collections

class HandshakePatternMatch:
def __init__(self):
self.moves = [None]
self.other = []
self.handshake = [None,"N","C","C","D","N"]
self.friendly = None
self.pattern = PatternFinder()
def round(self, last):
self.other.append(last)
if last:
if len(self.other) < len(self.handshake):
# still trying to handshake
if self.friendly == False or self.other[-1] != self.handshake[-1]:
self.friendly = False
else:
self.friendly = True
move = self.handshake[len(self.other)]
self.pattern.round(last)
elif self.friendly == True:
# successful handshake and continued cooperation
move = self.pattern.round(last)
if last == "C":
move = "C"
elif last == self.handshake[-1] and self.moves[-1] == self.handshake[-1]:
move = "C"
else:
self.friendly = False
else:
# failed handshake or abandoned cooperation
move = self.pattern.round(last)
else:
move = self.handshake[1]
self.pattern.round(last)
self.moves.append(move)
return move


Why pattern match yourself? Handshake and cooperate away.

• import PatternFinder is cheating in my books. – SIGSTACKFAULT Nov 17 '18 at 2:30
• @Blacksilver It gets done all the time in KOTH. It's no different than copying the code in an existing answer and using it. Robot Roulette: High stakes robot gambling had it happening all over the place to the point that bots would detect if their code was being called by an opponent and sabotage the return. – Draco18s no longer trusts SE Nov 17 '18 at 2:36
• Alright then. TIL. – SIGSTACKFAULT Nov 17 '18 at 2:37
• I'll do the crunching tomorrow. – SIGSTACKFAULT Nov 17 '18 at 2:38
• Here's a perfect example of using other bots' code. Usually it comes down to "that guy worked out some tricky math, I want his results under these conditions." (My own entry did that to pretty good effect; UpYours was more scatter-shot in its approach). – Draco18s no longer trusts SE Nov 17 '18 at 2:41

# Hardcoded

class Hardcoded:
sequence = "DNCNNDDCNDDDCCDNNNNDDCNNDDCDCNNNDNDDCNNDDNDDCDNCCNNDNNDDCNNDDCDCNNNDNCDNDNDDNCNDDCDNNDCNNDDCDCNNDNNDDCDNDDCCNNNDNNDDCNNDDNDCDNCNDDCDNNDDCCNDNNDDCNNNDCDNDDCNNNNDNDDCDNCDCNNDNNDDCDNDDCCNNNDNDDCNNNDNDCDCDNNDCNNDNDDCDNCNNDDCNDNNDDCDNNDCDNDNCDDCNNNDNDNCNDDCDNDDCCNNNNDNDDCNNDDCNNDDCDCNNDNNDDCDNDDCCNDNNDDCNNNDCDNNDNDDCCNNNDNDDNCDCDNNDCNNDNDDCNNDDCDNCNNDDCDNNDCDNDNCDDCNDNNDDCNNNDDCDNCNNDNNDDCNNDDNNDCDNCNDDCNNDCDNNDDCNNDDNCDCNNDNDNDDCDNCDCNNNDNDDCDCNNDNNDDCDNDDCCNNNDNNDDCNDNDNCDDCDCNNNNDNDDCDNCNDDCDNNDDCNNNDNDDCDNCNNDCNNDNDDNCDCDNNNDDCNNDDCNNDDNNDCDNCNDDCNNDDNDCDNNDNDDCCNCDNNDCNNDDNDDCNCDNNDCDNNNDDCNNDDCDCDNNDDCNDNCNNDNNDNDNDDCDNCDCNNNDNDDCDNCNNDDCDNNDCNNDDCNNDDCDCDNNDDCNDNCNNNDDCDNNDCDNDNCNNDNDDNNDNDCDDCCNNNDDCNDNDNCDDCDCNNNDNNDDCNDCDNDDCNNNNDNDDCCNDNNDDCDCNNNDNDDNDDCDNCCNNDNNDDCNNDDCDCNNDNNDDCNNDDNCNDDNNDCDNCNDDCNNDDNDCDNNDNDDCCNCDNNDCNNDNDDCNNDDNCDCDNNDCNNDNDDCDCDNNNNDDCNNDDNDCCNNDDNDDCNCDNNDCNNDDNDDCDNCNDDCNNNNDCDNNDDCNDNDDCDNCNNDCDNNDCNNDNDDNCDCNNDNDDCDNDDCCNNNNDNDDCNNDDCDCNNDNNDDCDCDNNDDC"
def __init__(self):
self.round_num = -1
def round(self,_):
self.round_num += 1
return Hardcoded.sequence[self.round_num % 1000]


Just plays a hardcoded sequence of moves optimized to beat some of the top deterministic bots.