Bayesian Programming and Learning for Multi-Player Video Games ...

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5.4.1 Bayesian unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 5.5 Results on StarCraft . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 5.5.1 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 5.5.2 Our bot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 5.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 5.6.1 Perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 5.6.2 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 6 Tactics 91 6.1 What are tactics? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 6.1.1 A tactical abstraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 6.1.2 Our approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 6.2 Related work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 6.3 Perception and tactical goals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 6.3.1 Space representation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 6.3.2 Evaluating regions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 6.3.3 Tech tree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 6.3.4 Attack types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 6.4 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 6.4.1 Source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 6.4.2 Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 6.4.3 Attacks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 6.5 A Bayesian tactical model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 6.5.1 Tactical Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 6.6 Results on StarCraft . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 6.6.1 Learning and posterior analysis . . . . . . . . . . . . . . . . . . . . . . . . 105 6.6.2 Prediction performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 6.6.3 Predictions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 6.6.4 Error analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 6.6.5 In-game decision-making . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110 6.7 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112 6.7.1 In-game learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112 6.7.2 Possible improvements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 6.7.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114 7 Strategy 117 7.1 What is strategy? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 7.2 Related work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 7.3 Perception and interaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 7.3.1 Buildings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 7.3.2 Openings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 7.3.3 Military units . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 7.4 Replays labeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 7.4.1 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 7.4.2 Probabilistic labeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 6
7.5 Build tree prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 7.5.1 Bayesian model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 7.5.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 7.5.3 Possible improvements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 7.6 Openings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 7.6.1 Bayesian model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 7.6.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 7.6.3 Possible uses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 7.7 Army composition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146 7.7.1 Bayesian model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146 7.7.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 7.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156 8 BroodwarBotQ 157 8.1 Code architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 8.1.1 Units control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158 8.1.2 Tactical goals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 8.1.3 Map and movements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 8.1.4 Technology estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 8.1.5 Enemy units filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160 8.2 A game walk-through . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164 8.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168 9 Conclusion 171 9.1 Contributions summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171 9.2 Perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173 9.2.1 Micro-management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173 9.2.2 Tactics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174 9.2.3 Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174 9.2.4 Inter-game Adaptation (Meta-game) . . . . . . . . . . . . . . . . . . . . . 176 Glossary 177 Bibliography 181 A Game AI 193 A.1 Negamax . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193 A.2 “Gamers’ survey” in section 2.9 page 39 . . . . . . . . . . . . . . . . . . . . . . . 193 B StarCraft AI 197 B.1 Micro-management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197 B.2 Tactics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198 B.2.1 Decision-making: soft evidences and coherence variables . . . . . . . . . . 198 B.3 Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204 B.4 BroodwarBotQ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204 7
Page 1: THÈSE Pour obtenir le grade de DOC
Page 4 and 5: Contents Contents 4 1 Introduction
Page 8 and 9: Notations Symbols ← assignment of
Page 10 and 11: Complexity, real-time constraints a
Page 12 and 13: intuition of Bayesian modeling to r
Page 14 and 15: e played by humans, by opposition t
Page 16 and 17: As a first approach, programmers ca
Page 18 and 19: 3 2 1 1 Figure 2.1: A Tic-tac-toe b
Page 20 and 21: Algorithm 2 Alpha-beta algorithm fu
Page 22 and 23: ewards on all the runs through node
Page 24 and 25: 2.4.1 Monopoly In Monopoly, there i
Page 26 and 27: 2.4.3 Poker Poker 4 is a zero-sum (
Page 28 and 29: 2.5.2 State of the art FPS AI consi
Page 30 and 31: 2.6.2 State of the art Methods used
Page 32 and 33: are no generic and efficient approa
Page 34 and 35: Strategy Tactics Action Strategic d
Page 36 and 37: 2.8.4 Time constant(s) For novice t
Page 38 and 39: effects. In RTS games, there is a l
Page 41 and 42: Chapter 3 Bayesian modeling of mult
Page 43 and 44: programmer-specified states, the (m
Page 45 and 46: which derives the laws of probabili
Page 47 and 48: Indeed, when evaluating two models
Page 49 and 50: • energy/mana/stamina regenerator
Page 51 and 52: • The probability that the ith un
Page 53 and 54: 3.3.4 Example This model has been a
Page 55 and 56: and P(Ai = false|T = i) = 0.6. To m
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Chapter 4 RTS AI: StarCraft: Broodw
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eplay is shown in appendix in Table
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Supply/Max supply Build Note (popul
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Figure 4.3: Military moves from a S
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Technology Strategy Army How? Econo
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• Robotic player (bot): chapter 8
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• Complexity: pspace-complete [Pa
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educing the complexity (no communic
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(grid-based) pathfinding was recent
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U A E Figure 5.4: In both figures,
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Fire Reload Figure 5.6: Fight FSM o
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• Obj i∈�1...n� ∈ {T rue,
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Identification Parameters and proba
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⎧⎪ Bayesian program ⎨ ⎧⎪
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36 units setup vs OAI. For Bayesian
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we learned, by optimizing the effic
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Probabilistic modality Finally, we
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• Type: prediction is problem of
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can possibly be in the future. In t
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6.3.2 Evaluating regions Partial ob
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Pylon Gate Core Range Gate Core Pyl
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events (detected by a heuristic, se
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• AD1:n ∈ {no, low, med, high}:
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The model is highly modular, and so
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attacked: P(ei�=r, ti�=r, tai
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Figure 6.7: P(A) for varying values
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Towards a baseline heuristic The me
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Figure 6.9 displays the mean P(A, H
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Finally, our approach is not exclus
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Chapter 7 Strategy Strategy without
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etween economy, technology and mili
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power to the player (it allows for
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7.4.2 Probabilistic labeling Instea
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• Variables: - X i∈�1...n�
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Figure 7.3: Protoss vs Terran distr
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7.5 Build tree prediction The work
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Questions The question that we will
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Table 7.4 shows the full results, t
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that this average “missed” (unp
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7.6 Openings 7.6.1 Bayesian model W
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Questions The question that we will
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Figure 7.12: Evolution of P(Opening
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Table 7.5: Prediction probabilities
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7.6.3 Possible uses We recall that
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• U t+1 ∈ ([0, 1] . . . [0, 1])
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(tt) if it allows for building all
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7.7.2 Results We did not evaluate d
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forces scores PvP PvT PvZ TvT TvZ Z
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shows a brutal transition from the
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Chapter 8 BroodwarBotQ Dealing with
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8.1.2 Tactical goals The decision t
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• X t i∈�1...n� ∈ �r1 .
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3 2 player 1 1 6 4 resources 8 play
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the construction plan as our Produc
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Figure 8.5: Crops of screenshots of
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anking). We consider that this rati
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This is an extension of the work on
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• differences in situations: as t
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9.2.4 Inter-game Adaptation (Meta-g
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fog of war hiding of some of the fe
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RTS Real-Time Strategy games are (m
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Sander C. J. Bakkes, Pieter H. M. S
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Julien Diard, Pierre Bessière, and
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Damian Isla. Handling complexity in
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Kinshuk Mishra, Santiago Ontañón,
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Mark Riedl, Boyang Li, Hua Ai, and
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Adrien Treuille, Seth Cooper, and Z
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• 0 (not at all, or irrelevant)
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Appendix B StarCraft AI B.1 Micro-m
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1|B, B ′ ) = 1.0 iff B = B ′ ,
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Figure B.2: Top: StarCraft’s Lost
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0,1 B' [0..5] T [0..2] E' 0,1 λ B
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C C A A C C A A 4 B B 4 3 4 B B 4 3
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