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

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RTS Real-Time Strategy games are (mainly) allocentric economic and military simulations from an operational tactical/strategist commander viewpoint, e.g. Command & Conquer, Age of Empires, StarCraft, Total Annihilation. 9, 10, 14, 35, 36 rush quick aggressive opening. 121 solved game a game whose outcome can be correctly predicted from any position when each side plays optimally. 17 StarCraft: Brood War a science fiction real-time strategy (RTS) game released in 1998 by Blizzard Entertainment. 14 supply cost in population (or supply) of a given unit, or current population count of a player. Originally, the Terran name for population/psi/control.. 59, 60, 100, 165 tech tree abbreviation for “technological tree”, state of the technology (buildings, researches, upgrades) which are unlocked/available to a given player.. 57, 59, 61, 66, 96–98, 111, 117–121, 146, 159, 165, 169, 172, 203 UCT Upper Confidence Bounds for Trees. 22, 114 zero-sum game a game in which the total score of each players, from one player’s point-ofview, for every possible strategies, adds up to zero; i.e. “a player benefits only at the expense of others”. 17 180
Bibliography David W. Aha, Matthew Molineaux, and Marc J. V. Ponsen. Learning to win: Case-based plan selection in a real-time strategy game. In Héctor Muñoz-Avila and Francesco Ricci, editors, ICCBR, volume 3620 of Lecture Notes in Computer Science, pages 5–20. Springer, 2005. ISBN 3-540-28174-6. 5 citations pages 63, 72, 94, 119, and 120 Srinivas M. Aji and Robert J. McEliece. The generalized distributive law. IEEE Transactions on Information Theory, 46(2):325–343, 2000. cited page 45 David W. Albrecht, Ingrid Zukerman, and Ann E. Nicholson. Bayesian models for keyhole plan recognition in an adventure game. User Modeling and User-Adapted Interaction, 8:5–47, January 1998. cited page 119 Victor L. Allis. Searching for Solutions in Games and Artificial Intelligence. PhD thesis, University of Limburg, 1994. URL http://fragrieu.free.fr/SearchingForSolutions.pdf. 2 citations pages 19 and 21 Christophe Andrieu, Nando De Freitas, Arnaud Doucet, and Michael I. Jordan. An introduction to mcmc for machine learning. Machine Learning, 50:5–43, 2003. cited page 46 M. Sanjeev Arulampalam, Simon Maskell, and Neil Gordon. A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking. IEEE Transactions on Signal Processing, 50:174–188, 2002. cited page 42 Robert B. Ash and Richard L. Bishop. Monopoly as a Markov process. Mathematics Magazine, (45):26–29, 1972. cited page 24 John Asmuth, Lihong Li, Michael Littman, Ali Nouri, and David Wingate. A bayesian sampling approach to exploration in reinforcement learning. In Uncertainty in Artificial Intelligence, UAI, pages 19–26. AUAI Press, 2009. 2 citations pages 82 and 87 Phillipa Avery, Sushil Louis, and Benjamin Avery. Evolving Coordinated Spatial Tactics for Autonomous Entities using Influence Maps. In Proceedings of the 5th international conference on Computational Intelligence and Games, CIG’09, pages 341–348, Piscataway, NJ, USA, 2009. IEEE Press. ISBN 978-1-4244-4814-2. URL http://dl.acm.org/citation.cfm?id= 1719293.1719350. 3 citations pages 73, 94, and 174 Sander Bakkes, Pieter Spronck, and Eric Postma. TEAM: The Team-Oriented Evolutionary Adaptability Mechanism. pages 273–282. 2004. URL http://www.springerlink.com/ content/hdru7u9pa7q3kg9b. 2 citations pages 16 and 72 181
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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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Page 137 and 138: 7.6 Openings 7.6.1 Bayesian model W
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Page 145 and 146: 7.6.3 Possible uses We recall that
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Page 149 and 150: (tt) if it allows for building all
Page 151 and 152: 7.7.2 Results We did not evaluate d
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Page 157 and 158: Chapter 8 BroodwarBotQ Dealing with
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Page 163 and 164: 3 2 player 1 1 6 4 resources 8 play
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Page 174 and 175: • differences in situations: as t
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Page 178 and 179: fog of war hiding of some of the fe
Page 182 and 183: Sander C. J. Bakkes, Pieter H. M. S
Page 184 and 185: Julien Diard, Pierre Bessière, and
Page 186 and 187: Damian Isla. Handling complexity in
Page 188 and 189: Kinshuk Mishra, Santiago Ontañón,
Page 190 and 191: Mark Riedl, Boyang Li, Hua Ai, and
Page 192 and 193: Adrien Treuille, Seth Cooper, and Z
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Page 197 and 198: Appendix B StarCraft AI B.1 Micro-m
Page 199 and 200: 1|B, B ′ ) = 1.0 iff B = B ′ ,
Page 201 and 202: Figure B.2: Top: StarCraft’s Lost
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Page 205 and 206: C C A A C C A A 4 B B 4 3 4 B B 4 3
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