The slides of Artificial Intelligence and Entertainment Science (AIES) Workshop 2021 Keynote lecture
https://aies.info/program/
Empathic Entertainment in Digital Game
A digital game give a unique experience to a user. AI system in Digital game consists of three kinds of AI such as Meta-AI, Character AI, and Spatial AI. Game experience is formed by them. Meta-AI keeps watching a status of game and controlling characters, objects, terrain, weather and so on dynamically to make many dramatic and empathic situations in a game for users. Character AI is a brain of an autonomous game character to make a decision by itself, but sometimes it acts to achieve a goal issued from Meta-AI. Spatial AI analyses a terrain and abstracts its features to communicate them to Meta-AI and Character-AI. They can make their intelligent decisions by using specific terrain and environment features. The AI system is called MCS-AI dynamic cooperative model (Meta-AI, Character AI, and Spatial AI dynamic cooperative model). In the lecture, I will explain the system by showing some cases of published digital games.
The document discusses the differences between making a microwave and creating artificial intelligence. It explores how intelligence may have common principles across different animals and how studying biology can help understand intelligence and realize it in computers and robots. It also discusses approaches to building AI through engineering as well as understanding what intelligence is through philosophy and science. Finally, it discusses game engines and their role in simulating physical, chemical, economic, social and biological rules to create virtual worlds.
17. IF (初めて話しかけられたら)THEN (“こんにちは。どっから来たの?”と言う)
IF (剣を買ってくれたら)THEN (“[それまでの剣の名前]よりいいわよ”と言う)
IF ([プレイヤーの薬草の数] が3つ以下)THEN (“薬草はどうですか?“と言う)
IF ([防具の使用時間]>10時間) THEN (“その[防具の名前]は新しくした方がいい”と言う)
※[] は知識表現のタグ。実際は知識表現から情報を入れる
IF (プレイヤーがピンチ)THEN (“大丈夫か?”と言う)
IF ([プレイヤーの体力]が30以下)THEN(”回復必要なんじゃない”と言う)
IF (味方が戦闘不能になる)THEN (“ちくしょう!“と言う)
IF(プレイヤーが敵を倒す)THEN (その[剣の名前]、切れ味いいんじゃない?)
※[] は知識表現のタグ。実際は知識表現から情報を入れる
街の商人のキャラクターの人工知能
戦闘中の仲間キャラクターの人工知能
図11
20. Jeff Orkin, “3 States and a Plan: The AI of F.E.A.R.",
http://web.media.mit.edu/~jorkin/gdc2006_orkin_jeff_fear.zip
敵キャラクター表現
kSymbol_AtNode ノードの上にいるか
kSymbol_TargetIsAimingAtMe こちらを狙っているか?
kSymbol_WeaponLoaded 装填されているか
kSymbol_WeaponArmed 武装しているか
kSymbol_AtNodeType どんなタイプのノードにいるか
kSymbol_RidingVehicle 乗り物に乗っているか
kSymbol_TargetIsSuppressed 威嚇されているか
kSymbol_UsingObject オブジェクトを使っているか?
kSymbol_TargetIsDead 生きているか
…
敵キャラクター表現
(図14)
32. AlphaGo
図19Mastering the game of Go with deep neural networks and tree search
http://www.nature.com/nature/journal/v529/n7587/full/nature16961.html
https://deepmind.com/research/alphago/
39. MIT Media Lab., Synthetic Characters Group,Duncan
the Highland Terrier / Sheep|Dog: Trial By Eire
http://characters.media.mit.edu/projects/duncan.html
図25http://characters.media.mit.edu/projects/duncan.html
43. Do you like the simulation game ? 98
du
do
d
u
y
yo
l
li
ry
ck
k
ky
th
z
ze
Sy
Si
zi
mu
m
uu
le
ra
ti
sy
on
an
a
g
Ju
zy
m
fu
do 76
dyu 34
li 63
k 87
ck34 z 34
th 94
sym 74
zi 64 la 65
ra 71sim 88
ty 23
te 93
ti 73
an 43
on 53
gu 33
gy 34
ge 74
mu 94
a
c
d
e do 88
you 86
Yaucht 76
like 62
luck 78
Zym 54
the 83
simulation 88
civilization 31
far 94
gam 54
simulation game 94
h Do you 89
you like 77
you luck 89
far civilization 78
the simulation 71
game 87
civilization game 94
jam 31
g
図27
ブラックボードによる階層的な音声解析
44. Do you like the simulation game ? 98
du
do
d
u
y
yo
l
li
ry
ck
k
ky
th
z
ze
Sy
Si
zi
mu
m
uu
le
ra
ti
sy
on
an
a
g
Ju
zy
m
fu
do 76
dyu 34
li 63
k 87
ck34 z 34
th 94
sym 74
zi 64 la 65
ra 71sim 88
ty 23
te 93
ti 73
an 43
on 53
gu 33
gy 34
ge 74
mu 94
a
c
d
e do 88
you 86
Yaucht 76
like 62
luck 78
Zym 54
the 83
simulation 88
civilization 31
far 94
gam 54
simulation game 94
h Do you 89
you like 77
you luck 89
far civilization 78
the simulation 71
game 87
civilization game 94
jam 31
g
図27
50. 膨大なユーザー群の
購入履歴データ
商品ID K L M N P Q R
評価 5 1 4 3 1 5 4ユーザーA
商品ID K L M N S T U
評価 4 2 5 2 4 3 5
ユーザーB
商品ID K L M N S T U
評価 5 2 4 1 5 1 5
ユーザーC
商品ID K L M N P T U
評価 5 1 5 4 1 2 5
ユーザーD
推
薦
推
薦
類似
購入履歴と評価の類似した
ユーザーを検索
図31
協調フィルタリング