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

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We did not kept at the research track of Bayesian modeling MMORPG games due to the difficulty to work on these types of games: the studios have too much to lose to “farmer” bots to accept any automated access to the game. Also, there are no sharing format of data (like replays) and the invariants of the game situations are fewer than in RTS games. Finally, RTS games have international AI competitions which were a good motivation to compare our approach with other game AI researchers. 56
Chapter 4 RTS AI: StarCraft: Broodwar We think of life as a journey with a destination (success, heaven). But we missed the point. It was a musical thing, we were supposed to sing and dance while music was being played. Alan Watts This chapter explains the basics of RTS gameplay*, particularly of StarCraft. We then list the (computational) challenges brought by RTS gameplay. We present a transversal decomposition of the RTS AI domain in levels of abstractions (strategy, tactics, micro-management*), which we will use for the rest of the dissertation. 4.1 How does the game work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 4.2 RTS AI challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 4.3 Tasks decomposition and linking . . . . . . . . . . . . . . . . . . . . . . . . . . 64 4.1 How does the game work 4.1.1 RTS gameplay We first introduce the basic components of a real-time strategy (RTS) game. The player is usually referred as the “commander” and perceives the world in an allocentric “God’s view”, performing mouse and keyboard actions to give orders to units (or squads of units) within a circumvented area (the “map”). In a RTS, players need to gather resources to build military units and defeat their opponents. To that end, they often have worker units (or extraction structures) that can gather resources needed to build workers, buildings, military units and research upgrades. Workers are often also builders (as in StarCraft) and are weak in fights compared to military units. Resources may have different uses, for instance in StarCraft: minerals* are used for everything, whereas gas* is only required for advanced buildings or military units, and technology upgrades. Buildings and research upgrades define technology trees (directed acyclic graphs) and each state of a tech tree* (or build tree*) allow for different unit type production abilities and unit spells/abilities. The military units can be of different types, any combinations of ranged, 57
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THÈSE Pour obtenir le grade de DOC
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Contents Contents 4 1 Introduction
Page 6 and 7: 5.4.1 Bayesian unit . . . . . . . .
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
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Page 41 and 42: Chapter 3 Bayesian modeling of mult
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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 P(Ai = false|T = i) = 0.6. To m
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Page 76 and 77: U A E Figure 5.4: In both figures,
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Page 80 and 81: • Obj i∈�1...n� ∈ {T rue,
Page 82 and 83: Identification Parameters and proba
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Page 86 and 87: 36 units setup vs OAI. For Bayesian
Page 88 and 89: we learned, by optimizing the effic
Page 90 and 91: Probabilistic modality Finally, we
Page 92 and 93: • Type: prediction is problem of
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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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