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

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• 0 (not at all, or irrelevant) • 1 (counts, can be game changing for people on equal level at other answers) • 2 (counts a lot, can make a player win even if he is a little worse on lower importance gameplay features) How important is inductive thinking? (to win the game) "He does A so he may be thinking B" or "I see D and E so (in general) he should be going for F (learned)", also called generalization, "abstraction". • 0 (not at all, or irrelevant) • 1 (counts, can be game changing for people on equal level at other answers) • 2 (counts a lot, can make a player win even if he is a little worse on lower importance gameplay features) How hard is decision-making? (to win the game) "I have options A, B and C, with regard to everything I know about this game, I will play B (to win)", selection of a course of actions. • 0 (not at all, or irrelevant) • 1 (counts, can be game changing for people on equal level at other answers) • 2 (counts a lot, can make a player win even if he is a little worse on lower importance gameplay features) You can predict the next move of your opponent: (is knowledge of the game or of the opponent more important) • 1 by knowing what the best moves are / the best play for him? • -1 by knowing him personally (psychology)? • 0 both equal What is more important: • 1 knowledge of the game (general strategies, tactics, timings) • -1 knowledge of the map (specific strategies, tactics, timings) • 0 both equal Results 194
Game Virtuosity Deduction Induction Decision-Making Opponent Knowledge (sensory-motor) (analysis) (abstraction) (acting) -1: subjectivity -1: map [0-2] [0-2] [0-2] [0-2] 1: objectivity 1: game top rest n top rest n top rest n top rest n top rest n top rest n Chess X X X 1.714 1.815 34 1.714 1.429 35 1.714 1.643 35 0.714 0.286 35 X X X Go X X X 2.000 1.667 16 2.000 1.600 16 2.000 1.600 16 1.000 0.533 16 X X X Poker X X X 1.667 0.938 22 1.667 1.562 22 1.333 1.625 22 -0.167 -0.375 22 X X X Racing 2.000 1.750 19 0.286 0.250 19 0.571 0.000 19 1.286 0.833 19 0.571 0.455 18 -0.286 -0.333 19 TeamFPS 1.917 1.607 40 1.083 1.000 39 1.417 0.929 40 1.417 1.185 39 0.000 0.214 40 -0.083 -0.115 38 FFPS 2.000 2.000 22 1.250 1.000 21 1.125 1.077 21 1.125 1.231 21 0.250 -0.154 21 0.250 0.083 20 MMORPG 1.118 1.000 29 1.235 0.833 29 1.176 1.000 29 1.235 1.250 29 0.471 0.250 29 0.706 0.833 29 RTS 1.941 1.692 86 1.912 1.808 86 1.706 1.673 83 1.882 1.769 86 0.118 0.288 86 0.412 0.481 86 195 Table A.1: Results of the survey (means) for “top” players and the “rest” of answers, along with the total number of answers (n).
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THÈSE Pour obtenir le grade de DOC
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Contents Contents 4 1 Introduction
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5.4.1 Bayesian unit . . . . . . . .
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Notations Symbols ← assignment of
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Complexity, real-time constraints a
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intuition of Bayesian modeling to r
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e played by humans, by opposition t
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As a first approach, programmers ca
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3 2 1 1 Figure 2.1: A Tic-tac-toe b
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Algorithm 2 Alpha-beta algorithm fu
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ewards on all the runs through node
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2.4.1 Monopoly In Monopoly, there i
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2.4.3 Poker Poker 4 is a zero-sum (
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2.5.2 State of the art FPS AI consi
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2.6.2 State of the art Methods used
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are no generic and efficient approa
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Strategy Tactics Action Strategic d
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2.8.4 Time constant(s) For novice t
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effects. In RTS games, there is a l
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Chapter 3 Bayesian modeling of mult
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programmer-specified states, the (m
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which derives the laws of probabili
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Indeed, when evaluating two models
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• energy/mana/stamina regenerator
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• The probability that the ith un
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3.3.4 Example This model has been a
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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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Page 186 and 187: Damian Isla. Handling complexity in
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Page 197 and 198: Appendix B StarCraft AI B.1 Micro-m
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