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

Recommendations

Info

Finally, our approach is not exclusive to most of the techniques presented above, and it could be interesting to combine it with Monte-Carlo planning, as Chung et al. [2005] did for capture-the-flag tactics in Open RTS. Also, UCT* is a Monte-Carlo planning algorithm allowing to build a sparse tree over the state tree (edges are actions, nodes are states) which had excellent results in Go [Gelly and Wang, 2006, Gelly et al., 2012]. Balla and Fern [2009] showed that UCT could be used in an RTS game (with multiple simultaneous actions) to generate tactical plans. Another track (instead of UCT) would be to use metareasoning for MCTS* [Hay and Russell, 2011], which would incorporate domain knowledge and/or hyper-parameters (aggressiveness...). 6.7.3 Conclusion We have presented a Bayesian tactical model for RTS AI, which allows both for opposing tactics prediction and autonomous tactical decision-making. Being a probabilistic model, it deals with uncertainty easily, and its design allows easy integration into multi-granularity (multi-scale) AI systems as needed in RTS AI. The advantages are that 1) learning will adapt the model output to its biased heuristic inputs 2) the updating process is the same for offline and online (in-game) learning. Without any temporal dynamics, the position prediction is above a baseline heuristic ([32.8-40.9%] vs [20.5-25.2%]). Moreover, its exact prediction rate of the joint position and tactical type is in [23-32.8]% (depending on the match-up), and considering the 4 most probable regions it goes up to ≈ 70%. More importantly, it allows for tactical decision-making under (technological) constraints and (state) uncertainty. It can be used in production thanks to its low CPU and memory footprint. 114
Table 6.2: Results summary for multiple metrics at 30 seconds before attack, including the percentage of the time that it is rightly what happened (% column). For the where question, we show the four most probable predictions, the “Pr” column indicates the mean probability of the each bin of the distribution. For the how question, we show the four types of attacks, their percentages of correctness in predictions (%) and the ratio of a given attack type against the total numbers of attacks ( type total ). The number in bold (38.0) is read as “38% of the time, the region i with probability of rank 1 in P(Ai) is the one in which the attack happened 30 seconds later, in the PvT match-up (predicting Protoss attacks on Terran)”. The associated mean probability for the first rank of the where measurement is 0.329. The percentage of good predictions of ground type attacks type in PvT is 98.1%, while ground type attacks, in this match-up, constitute 54% (ratio of 0.54) of all the attacks. The where & how line corresponds to the correct predictions of both where and how simultaneously (as most probables). NA (not available) is in cases for which we do not have enough observations to conclude sufficient statistics. Remember that attacks only include fights with at least 3 units involved. %: good predictions Protoss Terran Zerg Pr=mean probability P T Z P T Z P T Z total # games 445 2408 2027 2408 461 2107 2027 2107 199 measure rank % Pr % Pr % Pr % Pr % Pr % Pr % Pr % Pr % Pr 115 1 40.9 .334 38.0 .329 34.5 .304 35.3 .299 34.4 .295 39.0 0.358 32.8 .31 39.8 .331 37.2 .324 2 14.6 .157 16.3 .149 13.0 .152 14.3 .148 14.7 .147 17.8 .174 15.4 .166 16.6 .148 16.9 .157 3 7.8 .089 8.9 .085 6.9 .092 9.8 .09 8.4 .087 10.0 .096 11.3 .099 7.6 .084 10.7 .100 4 7.6 .062 6.7 .059 7.9 .064 8.6 .071 6.9 .063 7.0 .062 8.9 .07 7.7 .064 8.6 .07 measure type % type type type % % total total total % type % total type total % type % total type type type % % total total total G 97.5 0.61 98.1 0.54 98.4 0.58 100 0.85 99.9 0.66 76.7 0.32 86.6 0.40 99.8 0.84 67.2 0.34 A 44.4 0.05 34.5 0.16 46.8 0.19 40 0.008 13.3 0.09 47.1 0.19 14.2 0.10 15.8 0.03 74.2 0.33 I 22.7 0.14 49.6 0.13 12.9 0.13 NA NA NA NA 36.8 0.15 32.6 0.15 NA NA NA NA D 55.9 0.20 42.2 0.17 45.2 0.10 93.5 0.13 86 0.24 62.8 0.34 67.7 0.35 81.4 0.13 63.6 0.32 total 76.3 1.0 72.4 1.0 71.9 1.0 98.4 1.0 88.5 1.0 60.4 1.0 64.6 1.0 94.7 1.0 67.6 1.0 where & how (%) 32.8 23 23.8 27.1 23.6 30.2 23.3 30.9 26.4 where how
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 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 176 and 177:
9.2.4 Inter-game Adaptation (Meta-g
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 ...

Create successful ePaper yourself

Delete template?

Save as template?