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

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Probabilistic modality Finally, we could use multi-modality [Colas et al., 2010] to get rid of the remaining (small: fireretreat-move) FSM. Instead of being in “hard” modes, the unit could be in a weighted sum of modes (summing to one) and we would have: P(Dir) = w fightMove()P(Dir|sensory_inputs) fightMove()+w flee()P(Dir|sensory_inputs) flee() . . . This could particularly help dealing with the fact that parameters learned from fixed scenarii would be too specialized. This way we could interpolate a continuous family of distributions for P(Dir) from a fixed and finite number of parameters learned from a finite number of experiments setups. 5.6.2 Conclusion We have implemented this model in StarCraft, and it outperforms the original AI as well as other bots. Our approach does not require a specific vertical integration for each different type of objectives (higher level goals), as opposed to CBR and reactive planning [Ontañón et al., 2007a, Weber et al., 2010b]: it can have a completely different model above feeding sensory inputs like Obji. It runs in real-time on a laptop (Core 2 Duo) taking decisions for every units 24 times per second. It scales well with the number of units to control thanks to the absence of communication at the unit level, and is more robust and maintainable than a FSM. Particularly, the cost to add a new sensory input (a new effect on the units behavior) is low. 90
Chapter 6 Tactics It appears to be a quite general principle that, whenever there is a randomized way of doing something, then there is a nonrandomized way that delivers better performance but requires more thought. E.T. Jaynes (Probability Theory: The Logic of Science, 2003) We present a Bayesian model for opponent’s tactics prediction and tactical decision-making in real-time strategy games. We first give our definitions of tactics and an overview of the related works on tactical elements of RTS games. We then present the useful perceptions and decisions relative to tactics. We assembled a dataset for which we present information available and how we collected it. Then, the detail of the Bayesian program is presented. The main idea is to adapt the model to inputs from (possibly) biased heuristics. We evaluated the model in prediction of the enemy tactics on professional gamers data. This work was accepted for publication at Computational Intelligence in Games (IEEE CIG) 2012 in Grenada [Synnaeve and Bessière, 2012a] and will be presented at the Computer Games Workshop of the European Conference of Artificial Intelligence (ECAI) 2012 [Synnaeve and Bessière, 2012b]. 6.1 What are tactics? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 6.2 Related work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 6.3 Perception and tactical goals . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 6.4 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 6.5 A Bayesian tactical model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 6.6 Results on StarCraft . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 6.7 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112 • Problem: make the most efficient tactical decisions (attacks and defenses) in the absolute (knowing everything: armies positions, players intentions, effects of each possible actions). • Problem that we solve: make the most efficient tactical decisions (in average) knowing what we saw from the opponent and our model of the game. 91
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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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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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