Creating Strong AIWolf
Agents using Systematic
Tactical Evaluation
SHUNSUKE HATTA, KEISUKE TAKAHASHI, KAZUNARIFURUKAWA, JUNJI YAMATO

KOGAKUIN UNIVERSITY, TOKYO, JAPANContents
 1.Introduction
 2.Objectives
 3. Experimental Procedures
 4. Results
 5. Discussion

 6. Conclusion

1.Introduction
 Game with perfect information ・・・all players

can observe all previous decisions made by allplayers. AI > human→GO and Shogi
 Game with imperfect information ・・・allplayers cannot observe all previous decisions madeby all players. Ai < human→mahjong and poker

1.Introduction
 Among them, the “AIWolf Project" is a project toconduct AI research using the communication
game "werewolf game".
 The AIWolf Project is holding international

competitions once a year starting in 2019.
 We created a variety of agents to achieve excellent

results in the "Protocol division" of AIWolf.

2.Objectives
 The purpose of this study is・To evaluate the effectiveness of selecting agents byevaluating their tactics.
・To find effective tactics for each role.
・To submit strong agents to the “3rd International AI

Werewolf Competition” in conjunction with IJCAI
2021.

3. Experimental Procedures
3.1 Agent Selection
 The roles used are the same as in the real

tournament Chosen randomly in each game．
 8 "villager", 3 "werewolf", 1 "seer", 1 "possessed",1 "bodyguard", 1 "medium”.

3. Experimental Procedures
3.1 Agent Selection
 We created several agents each role with different

combinations of tactics and compared theirwinning rate.
 Instead of implementing tactics that are considered

to be strong , we selected strong agents throughevaluation experiments.
 We used several agents created using agents

creation system called the "Simple GenerationSystem" .

3. Experimental Procedures
3.2 Evaluation Methods
 We prepared three base agents, Agnet1,2,3, andcreated multiple agents based on them.
 We played 100 games, 10 times each, for eachagent to be evaluated.
 The opponents, 14 agents were selected mainly

from the agents of the teams participating in the"International AIWolf Competition. "3. Experimental Procedures
3.2 Evaluation Methods(Hachi) Hachi is an agent based on Agent1.

 In a 5-player village, we selected the tactic of

speaking out aggressively.
 In the 15-player village, we focused mainly on thetactics of the werewolf team. We analyzed theconcept of "Hiding," .

3. Experimental Procedures
3.2 Evaluation Methods(Hachi)
 Hiding is not to do many conspicuous actions andleave the early stages to other fellow werewolves.
 We paid particular attention to the reported resultsof deceiving a seer when it was a possessed.
Winning rate

Number of time

Behaving like a real role

0.36

5

More predict that werewolf

0.25

4

More predict that werewolf as villager

0.36

 After analysis, we selected the tactic of behaving

like a real role.

3. Experimental Procedures
3.2 Evaluation Methods(KP22)
 KP22 is designed based on Agent2.

 In a 5-player village, we selected the tactic ofhiding.
 In a 15-player village, we analyzed the concept oftaking the tactic of "disrupting the village".

3. Experimental Procedures
3.2 Evaluation Methods(KP22)
 we paid particular attention to the tactics of thewerewolf to deceive a seer.
 After analysis, we selected the tactic of predictingthat werewolf regardless of allied werewolves.

3. Experimental Procedures
3.2 Evaluation Methods(Tomatoken)
 Tomatoken is designed based on Agent3.
 In both the 5-player and 15-player village, we

selected the tactic of adjust to other players when it
was a werewolf.
 We also paid particular attention to the medium'stactics, selecting not to act in a way that would
protect his allies.

4. Results
4.1 Selection Results(5-player village)
5-player village
1. Voting Policy
1.
1.

1.
1.
1.

1.

1.

2. Protecting
seer in voting
3. Speeching
Policyvillager

H,K: For werewolf
T: Balance

Yes

Yes

H,K: Expresssuspicious people
T: Balance

H,K: Express
suspicious people
T: Balance

4. Predicting
and Attacking
Policy5. About 4 in
the winningrate
6. Predicting
policy7. Deceiving
seer policy
8. Learningseer

H,K: For werewolf
T: Balance

werewolf

possessed

H,K: Adjust to otherplayers
T: Balance
H,T: Yes
K: No

H,K: Adjust to otherplayers
T: Balance
H,T: Yes
K: No

Player who think is awerewolf

H: Express suspiciouspeople
K,T: Act like otherplayers
H: The seerK,T: Balance

H,T: Express suspicious
peopleK: Act like otherplayers

H,K: Higher winning
rate player
K: No weighting

H,K: Higher winning
rate player
K: No weighting

Communicate
honestly

H,K: More learning

More learning

H: More predict thatwerewolf
K,T: Behave like a realrole
H,K: More learning

H,K: Predict thatvillager for werewolf
T: Behave like a realrole
More learning4. Results
4.1 Selection Results(15-player village)
15-player village
1. Voting Policyvillager
H,K: For werewolf
T: Balanceseer
H,K: For werewolf
T: Balancewerewolf
H: Adjust to other players
K,T: Balance

2. Protecting seer, medium,
bodyguard in voting

Yes

Yes

H,T: Yes
K: No

3. cut off relations withallies
4. Speeching Policy

H,K: Expresssuspicious people
T: Balance

H,K: Express suspicious
peopleT: Balance

H,K: Yes
T: No
H,T: Act like other players H: Act like other
K: Express suspicious
players
peopleK: Balance
T: Express suspicious
peopleH,T: Balance
K: The seer

5. Predicting and Protecting
and Attacking Policy

H: A player of suspected
K,T: Player who think isa werewolf

6. About 5 in the winningrate

H,K: Higher winning rate H,K: Higher winning rate
playerplayer
K: No weighting
K: No weighting
Communicate honestly

7. Predicting policy

8. Deceiving seer policy

H: More predict thatvillager
K: More predict thatwerewolf
T:Behaving like a real role

9. Treatment of allied
werewolf in 8

H: Not care werewolf ornot
K,T: Predict that villager
for werewolf
H,K: More learning
T: Not much learning

10. Learning

H,K: More learning
T: No learning

More learning

possessed
H: Adjust to otherplayers
K,T: BalanceH,T: Yes
K: No

mediumH,K: For werewolf
T: Adjust to other players

bodyguard
H,K: For werewolf
T: Balance

H,K: Yes
T: NoYes

Express suspicious people

H,K: Expresssuspicious people
T: Balance

H,K: The seer
T: Balance
H,K: Higher winning
rate player
K: No weighting

H,T: Behaving like areal role
K: More predict thatvillager

More learning

More learning

More learning4. Results
4.2 Tournament Results
Rank

Agent Name

Programming Language

1234567891011121314

toku/ICETOTKP22Syu
CanisLapusTomatoken
SORAHideto
HALU
TomatoOKAMI
karma
wasabi
Sashimi

JavaC#/ JavaJava
Java
Java
Java
Java
Java
pythonJavapython
JavaJavaJava

UsingaSimple
Generation System✓✓✓✓✓✓ Hachi did not qualify, KP22 finished 3rd, andTomatoken finished 6th. indicating that the overall
results were good.

5. Discussion
 The stronger agent is…

・Actively identifying suspicious agent.
・Proactively predicting that werewolf when
deceiving a seer.
・Randomly attacking without learning much.

5. Discussion
 In addition, the adoption of somewhat

unconventional tactics and the combination ofagents may have something to do with the winningrate.
 From that fact, there might be a kind of"Metagame" in which we take into account ourown composition by predicting the composition ofour opponents.

6. Conclusion
 In order to improve the win rate of AI in the"Protocol division" of AIWolf, we created agentsbased on Agent1,2,3 and analyzed the win rate.
 Two of them made it to the finals, and the best

result was 3rd place.

6. Conclusion
 We would like to challenge research on the"Natural language division".
 The division is evaluates whether AI is capable ofnatural dialogue, with the aim of having AI andhumans fight in werewolf game.
 See you again at the 4th International AI Werewolf

Competition.

Thank you for listening!