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

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7.8 Conclusion We contributed a probabilistic model to be able to compute the distribution over openings (strategies) of the opponent in a RTS game from partial and noisy observations. The bot can adapt to the opponent’s strategy as it predicts the opening with 63 − 68% of recognition rate at 5 minutes and > 70% of recognition rate at 10 minutes (up to 94%), while having strong robustness to noise (> 50% recognition rate with 50% missing observations). It can be used in production due to its low CPU (and memory) footprint. We also contributed a semi-supervised method to label RTS game logs (replays) with openings (strategies). Both our implementations are free software and can be found online 10 . We use this model in our StarCraft AI competition entry bot as it enables it to deal with the incomplete knowledge gathered from scouting. We presented a probabilistic model inferring the best army composition given what was previously seen (from replays, or previous games), integrating adaptation to the opponent with other constraints (tactics). One of the main advantages of this approach is to be able to deal natively with incomplete information, due to player’s intentions, and to the fog of war in RTS. The army composition dimensionality reduction (clustering) can be applied to any game and coupled with other techniques, for instance for situation assessment in case-based planning. The results in battle outcome prediction (from few information) shows its situation assessment potential. 10 https://github.com/SnippyHolloW/OpeningTech/ 156
Chapter 8 BroodwarBotQ Dealing with failure is easy: Work hard to improve. Success is also easy to handled: You’ve solved the wrong problem. Work hard to improve. Alan J. Perlis (1982) In this chapter, we present some of the engineering that went in the robotic player (bot) implementation, which may help the comprehension of the organization and utility of the different chapters. We will also present the different flows of informations and how decisions are made during a game. Finally we will present the results of the full robotic player to various bots competitions. 8.1 Code architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 8.2 A game walk-through . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164 8.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168 8.1 Code architecture Our implementation 1 (BSD licensed) uses BWAPI 2 to get information from and to control StarCraft. The bot’s last major revision (January 2011) consists of 23,234 lines of C++ code, making good use of boost libraries and BWTA (Brood War Terrain Analyzer). The learning of the parameters from the replays is done by separated programs, which serialize the probability tables and distribution parameters, later loaded by BroodwarBotQ each game. The global (simplified) view of the whole bot’s architecture is shown in Figure 8.1. There are three main divisions: “Macro” (economical and production parts), “Intelligence” (everything information related) and “Micro” (military units control/actions). Units (workers, buildings, military units) control is granted through a centralized Arbitrator to “Macro” parts and Goals. This is a bidding system in which parts wanting a unit bid on it relatively to the importance of the task they will assign it. There may (should) be better systems but it works. We will now detail the parts which were explained in the previous three chapters. 1 BroodwarBotQ, code and releases: http://github.com/SnippyHolloW/BroodwarBotQ 2 BWAPI: http://code.google.com/p/bwapi/ 157
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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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Page 133 and 134: Table 7.4 shows the full results, t
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Page 137 and 138: 7.6 Openings 7.6.1 Bayesian model W
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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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Page 201 and 202: Figure B.2: Top: StarCraft’s Lost
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