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

Recommendations

Info

9.2.4 Inter-game Adaptation (Meta-game) In a given match, and/or against a given player, players tend to learn from their immediate mistakes, and they adapt their strategies to each other’s. As this can be seen as a continuous learning problem (reinforcement learning, exploration-exploitation trade-off), there is more to it. Human players call this the meta-game*, as they enter the “I think that he thinks that I think...” game until arriving at fixed points. This “meta-game” is closely related to the balance of the game, and the fact that there are several equilibriums makes the interest of StarCraft. Also, clearly, for human players there is psychology involved. Continuous learning For all strategic models, the possible improvements (subsections 7.5.3, 7.6.3, 7.7.2) would include to learn specific sets of parameters against the opponent’s strategies. The problem here is that (contrary to battles/tactics) there are not much observations (games against a given opponent) to learn from. For instance, a naive approach would be to learn a Laplace’s law of succession directly on P (ET echT rees = ett|P layer = p) = 1+nbgames(ett,p) #ET T +nbgames(p) , and do the same for EClusters, but this could require several games. Even if we see more than one tech tree per game for the opponent, a few games will still only show a sparse subset of ET T . Another part of this problem could arise if we want to learn really in-game as we would only have partial observations. Manish Meta [2010] approached “meta-level behavior adaptation in RTS games” as a mean for their case-based planning AI to learn from its own failures in Wargus. The opponent is not considered at all, but this could be an interesting entry point to discover bugs or flaws in the bot’s parameters. Bot’s psychological warfare Our main idea for real meta-game playing by our AI would be to use our models recursively. As for some of our models (tactics & strategy), that can be used both for prediction and decisionmaking, we could have a full model of the enemy by maintaining the state of a model from them, with their inputs (and continually learn some of the parameters). For instance, if we have our army adaptation model for ourselves and for the opponent, we need to incorporate the output of their model as an input of our model, in the part which predicts the opponent’s future army composition. If we cycle (iterate) the reasoning (“I will produce this army because they will have this one”...), we should reach these meta-game equilibriums. Final words Finally, bots are as far from having a psychological model of their opponent as from beating the best human players. I believe that this is our adaptability, our continuous learning, which allows human players (even simply “good” ones like me) to beat RTS games bots consistently. When robotic AI will start winning against human players, we may want to hinder them to have only partial vision of the world (as humans do through a screen) and a limited number of concurrent actions (humans use a keyboard and a mouse so they have limited APM*). At this point, they will need an attention model and some form of hierarchical action selection. Before that, all the problems arose in this thesis should be solved, at least at the scale of the RTS domain. 176
Glossary AI directors system that overlooks the behavior of the players to manage the intensity, difficulty and fun. 15 APM action per minute, an input speed frequency in competitive gaming. 36, 99, 176 Bayesian game a game in which information about knowledge about payoffs is incomplete. 26, 31 BIC Bayesian information criterion, a score for model selection. For a given model with n data points, k parameters and L the maximum likelihood, BIC = −2 ln(L) + k ln(n). 124, 125, 154 BP Bayesian program. 45 branching factor (average) number of nodes at each level of a search tree, i.e. base b of the complexity of a search of depth d in a tree, which is O(b d ). 9, 19–21, 32 build order a formal specification of timings (most often indexed on total population count) at which to perform build actions in the early game.. 59, 120, 123 build tree abbrev. for “buildings tree”, state of the buildings (and thus production) unlocked by a player. 57, 117, 119, 120, 129, 137, 141, 145, 146, 159, 172 BWAPI Brood War Application Programmable Interface. 99 BWTA BroodWar Terrain Analyser. 95, 201 CBR Case-Based Reasoning. 94, 120, 174 Dark Spore a fast-paced, sci-fi action-RPG, with PvP and cooperative (vs AI) modes. 15 DSL Domain Specific Language. 16 EM expectation-maximization, an iterative method to optimize parameters of statistical models depending on unobserved variables. The expectation (E) step gives the likelihood depending on the latent variables, and the maximization (M) step computes maximizing parameters for the expectation of this likelihood.. 123–125, 161 expand either placement of a new base or the action to take a new base (to collect more resources).. 65, 165, 173 177
Page 1:
THÈSE Pour obtenir le grade de DOC
Page 4 and 5:
Contents Contents 4 1 Introduction
Page 6 and 7:
5.4.1 Bayesian unit . . . . . . . .
Page 8 and 9:
Notations Symbols ← assignment of
Page 10 and 11:
Complexity, real-time constraints a
Page 12 and 13:
intuition of Bayesian modeling to r
Page 14 and 15:
e played by humans, by opposition t
Page 16 and 17:
As a first approach, programmers ca
Page 18 and 19:
3 2 1 1 Figure 2.1: A Tic-tac-toe b
Page 20 and 21:
Algorithm 2 Alpha-beta algorithm fu
Page 22 and 23:
ewards on all the runs through node
Page 24 and 25:
2.4.1 Monopoly In Monopoly, there i
Page 26 and 27:
2.4.3 Poker Poker 4 is a zero-sum (
Page 28 and 29:
2.5.2 State of the art FPS AI consi
Page 30 and 31:
2.6.2 State of the art Methods used
Page 32 and 33:
are no generic and efficient approa
Page 34 and 35:
Strategy Tactics Action Strategic d
Page 36 and 37:
2.8.4 Time constant(s) For novice t
Page 38 and 39:
effects. In RTS games, there is a l
Page 41 and 42:
Chapter 3 Bayesian modeling of mult
Page 43 and 44:
programmer-specified states, the (m
Page 45 and 46:
which derives the laws of probabili
Page 47 and 48:
Indeed, when evaluating two models
Page 49 and 50:
• energy/mana/stamina regenerator
Page 51 and 52:
• The probability that the ith un
Page 53 and 54:
3.3.4 Example This model has been a
Page 55 and 56:
and P(Ai = false|T = i) = 0.6. To m
Page 57 and 58:
Chapter 4 RTS AI: StarCraft: Broodw
Page 59 and 60:
eplay is shown in appendix in Table
Page 61 and 62:
Supply/Max supply Build Note (popul
Page 63 and 64:
Figure 4.3: Military moves from a S
Page 65 and 66:
Technology Strategy Army How? Econo
Page 67:
• Robotic player (bot): chapter 8
Page 70 and 71:
• Complexity: pspace-complete [Pa
Page 72 and 73:
educing the complexity (no communic
Page 74 and 75:
(grid-based) pathfinding was recent
Page 76 and 77:
U A E Figure 5.4: In both figures,
Page 78 and 79:
Fire Reload Figure 5.6: Fight FSM o
Page 80 and 81:
• Obj i∈�1...n� ∈ {T rue,
Page 82 and 83:
Identification Parameters and proba
Page 84 and 85:
⎧⎪ Bayesian program ⎨ ⎧⎪
Page 86 and 87:
36 units setup vs OAI. For Bayesian
Page 88 and 89:
we learned, by optimizing the effic
Page 90 and 91:
Probabilistic modality Finally, we
Page 92 and 93:
• Type: prediction is problem of
Page 94 and 95:
can possibly be in the future. In t
Page 96 and 97:
6.3.2 Evaluating regions Partial ob
Page 98 and 99:
Pylon Gate Core Range Gate Core Pyl
Page 100 and 101:
events (detected by a heuristic, se
Page 102 and 103:
• AD1:n ∈ {no, low, med, high}:
Page 104 and 105:
The model is highly modular, and so
Page 106 and 107:
attacked: P(ei�=r, ti�=r, tai
Page 108 and 109:
Figure 6.7: P(A) for varying values
Page 110 and 111:
Towards a baseline heuristic The me
Page 112 and 113:
Figure 6.9 displays the mean P(A, H
Page 114 and 115:
Finally, our approach is not exclus
Page 117 and 118:
Chapter 7 Strategy Strategy without
Page 119 and 120:
etween economy, technology and mili
Page 121 and 122:
power to the player (it allows for
Page 123 and 124:
7.4.2 Probabilistic labeling Instea
Page 125 and 126: • Variables: - X i∈�1...n�
Page 127 and 128: Figure 7.3: Protoss vs Terran distr
Page 129 and 130: 7.5 Build tree prediction The work
Page 131 and 132: Questions The question that we will
Page 133 and 134: Table 7.4 shows the full results, t
Page 135 and 136: that this average “missed” (unp
Page 137 and 138: 7.6 Openings 7.6.1 Bayesian model W
Page 139 and 140: Questions The question that we will
Page 141 and 142: Figure 7.12: Evolution of P(Opening
Page 143 and 144: Table 7.5: Prediction probabilities
Page 145 and 146: 7.6.3 Possible uses We recall that
Page 147 and 148: • U t+1 ∈ ([0, 1] . . . [0, 1])
Page 149 and 150: (tt) if it allows for building all
Page 151 and 152: 7.7.2 Results We did not evaluate d
Page 153 and 154: forces scores PvP PvT PvZ TvT TvZ Z
Page 155 and 156: shows a brutal transition from the
Page 157 and 158: Chapter 8 BroodwarBotQ Dealing with
Page 159 and 160: 8.1.2 Tactical goals The decision t
Page 161 and 162: • X t i∈�1...n� ∈ �r1 .
Page 163 and 164: 3 2 player 1 1 6 4 resources 8 play
Page 165 and 166: the construction plan as our Produc
Page 167 and 168: Figure 8.5: Crops of screenshots of
Page 169: anking). We consider that this rati
Page 172 and 173: This is an extension of the work on
Page 174 and 175: • differences in situations: as t
Page 178 and 179: fog of war hiding of some of the fe
Page 180 and 181: RTS Real-Time Strategy games are (m
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
Page 194 and 195: • 0 (not at all, or irrelevant)
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
Page 203 and 204: 0,1 B' [0..5] T [0..2] E' 0,1 λ B
Page 205 and 206: C C A A C C A A 4 B B 4 3 4 B B 4 3
show all

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

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?