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

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• differentiation between trajectories of ground and flying units, • differentiation between different trajectories of attacks inside large regions. We would like to implement our regions-based enemy units filtering in the future, mainly for more accurate and earlier tactical prediction (perhaps even drop tactics interception) and better tactical decision making. 8.2 A game walk-through We will now show key moments of the game, as was done with a human-played game in section 4.1.2, but from the bot’s point of view (with debug output). Figure 8.3: A full screen capture of some debug output of economical parts of BroodwarBotQ. The orange text at the top left shows the minerals/minute and gas/minute rates as well as the resources reserved for planned buildings and additional supply. The teal (light blue) rectangle at the bottom of the game’s view shows the buildings that will be constructed (and their future tentative positions). The big transparent (translucent) blue ellipses show the Pylons construction coverage. The yellow rectangles (with numbers in their top left corners) show the future buildings planned positions. First, Figure 8.3 shows some economical elements of BBQ. The yellow rectangles show future buildings placements. In the light blue (teal color) rectangle are the next buildings which are planned for construction with their future positions. Here, the Protoss Pylon was just added to 164
the construction plan as our Producer estimated that we will be supply* blocked in 28 seconds at the current production rate. This is noted by the “we need another pylon: prevision supply 84 in 28 sec” line, as supply is shown doubled (for programmatic reasons, so that supply is always an integer): 84 is 42, and we have a current max supply* of 41 (top right corner). Our buildings placer make it sure that there is always a path around our buildings blocks with a flow algorithm presented in the appendix in algorithm 9 and Figure B.7. It is particularly important to be able to circulated in the base, and that newly produced units are not stuck when they are produced (as they could is there was a convex enclosure as in Fig. B.7). Buildings cannot be placed anywhere on the map, even more so for Protoss buildings, as most of them need to be build under Pylon coverage (the plain blue ellipses in the screenshot). At the same time, there are some (hard) tactical requirements for the placement of defensive buildings 3 . At the beginning of the game, we have no idea about what the opponent is doing and thus our belief about their opening equals the prior (here, we set the priori to be uniform, see section 7.6.3 for how we could set it otherwise) we send a worker unit to “scout” the enemy’s base both to know where it is and what the enemy is up to. Figure 8.4 shows when we first arrive at the opponent’s base in a case in which we have a strong evidence of what the opponent is doing and so our beliefs are heavily favoring the “Fast Legs” opening (in the blue rectangle on the right). We can see that with more information (the bottom picture) we make an even stronger prediction. If our bot does not have to defend an early rush by the opponent, it chooses to do a “push” (a powerful attack towards the front of the opponent’s base) once it has a sufficient amount of military units, while it expands* (take a second base) or opens up its tech tree*. The first push is depicted in Figure 8.5: • First (top), we regroup our forces in front of the opponent’s base with a formation SubGoal. • By trying to enter the opponent’s base (second screenshot), our units have to fight they way through. • The bottom left picture shows the distribution on P(Diri) for the possible movements directions of one of the units at the top. We can see that it wants to avoid collision with allied units while going towards its target. • The bottom right picture shows our units “kiting back” (retreating while fighting) to avoid being exposed in the ramp up the cliff (right part of the image). BroodwarBotQ then goes up the ramp and destroys the base of the built-in AI. 3 A good benchmark for buildings positioning would be to test if an AI can perform a “Forge expand” which consists in blocking all ground paths with buildings and protecting against the early rushes with judiciously placed Photon Cannons, without any military unit. 165
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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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Page 123 and 124: 7.4.2 Probabilistic labeling Instea
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Page 149 and 150: (tt) if it allows for building all
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Page 163: 3 2 player 1 1 6 4 resources 8 play
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Page 180 and 181: RTS Real-Time Strategy games are (m
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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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