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Fire Reload Figure 5.6: Fight FSM of a simple unit model: Heuristic Only AI (HOAI), which will serve as a baseline for benchmarks. 5.3.3 Units group The units group (UnitsGroup, see Fig. 5.1) makes the shared future damage data structure available to all units taking part in a fight. Units control is not limited to fight. As it is adversarial, it is perhaps the hardest task, but units control problems comprehends efficient team maneuvering and other behaviors such as scouting or staying in position (formation). The units group structure helps for all that: it decides of the modes in which the unit has to be. We implemented 4 modes in our Bayesian unit (see Fig. 5.7): fight, move, scout, inposition. When given a task, also named goal, by the tactician model (see Fig. 5.1), the units group has to transform the task objective into sensory inputs for the units. • In scout mode, the (often quick and low hit points) unit avoids danger by modifying locally its pathfinding-based, objectives oriented route to avoid damages. • In move mode, the objective is extracted for the pathfinder output: it consists in key waypoints near the units. When moving by flocking, our unit moves are influenced by other near allied units that repulse or attract it depending on its distance to the interpolation of the allied unit. It allows our units to move more efficiently by not splitting around obstacles and colliding less. As it causes other problems, we do not use the flocking behavior in full competitive games. • In the inposition mode, the objective is the final unit formation position. The unit can be “pushed” by other units wanting to pass through. This is useful at a tactical level to do a wall of units that our units can traverse but the opponent’s cannot. Basically, there is an attraction to the position of the unit and a stronger repulsion of the interpolation of movements of allied units. • In fight mode, our unit will follow the damages gradient to smart positions, for instance close to tanks (they cannot fire too close to their position) or far from too much contact units if our unit can attack with range (something called “kiting”). Our unit moves are also influenced by its priority targets, its goal/objective (go through a choke, flee, etc.) and other units. The objective depends of the confidence of the units group in the outcome of the battle: Move – If the units group is outnumbered (in adjusted strength) and the task is not a suicidal one, the units group will “fall back” and give objectives towards retreat. 78 Flee
– If the fight is even or manageable, the units group will not give any objective to units, which will set their own objectives either to their priority targets in fightMove() or towards fleeing damages (towards lowest potential damages gradient) in flee(). – If the units group is very confident in winning the fight, the objectives sensory inputs of the units will be set at the task objectives waypoints (from the pathfinder). 5.4 A Bayesian model for units control 5.4.1 Bayesian unit We use Bayesian programming as an alternative to logic, transforming incompleteness of knowledge about the world into uncertainty. In the case of units management, we have mainly intensional uncertainty. Instead of asking questions like: where are other units going to be 10 frames later? Our model is based on rough estimations that are not taken as ground facts. Knowing the answer to these questions would require for our own (allied) units to communicate a lot and to stick to their plan (which does not allow for quick reaction nor adaptation). We propose to model units as sensory-motor robots described within the Bayesian robot programming framework [Lebeltel et al., 2004]. A Bayesian model uses and reasons on distributions instead of predicates, which deals directly with uncertainty. Our Bayesian units are simple hierarchical finite states machines (states can be seen as modes) that can scout, fight and move (see Figure 5.7). Each unit type has a reload rate and attack duration, so their fight mode will be as depicted in Figure 5.6 and Algorithm 5.5. The difference between our simple HOAI presented above and Bayesian units are in flee() and fightMove() functions. Fight Move Scout In Position Fire Figure 5.7: Bayesian unit modal FSM (HFSM*), detail on the fight mode. Stripped modes are Bayesian. The unit needs to determine where to go when fleeing and moving during a fight, with regard to its target and the attacking enemies, while avoiding collisions (which results in blocked units and time lost) as much as possible. We now present the Bayesian program used for flee(), fightMove(), and while scouting, which differ only by what is inputed as objective: Variables • Dir i∈�1...n� ∈ {T rue, F alse}: at least one variable for each atomic direction the unit can go to. P(Diri = T rue) = 1 means that the unit will certainly go in direction i (⇔ � di). For example, in StarCraft we use the 24 atomic directions (48 for the smallest and fast units as we use a proportional scale) plus the current unit position (stay where it is) as shown in Figure 5.2. 79 Move Reload Flee
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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 (
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
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Page 34 and 35: Strategy Tactics Action Strategic d
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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
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Page 65 and 66: Technology Strategy Army How? Econo
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Page 72 and 73: educing the complexity (no communic
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Page 76 and 77: U A E Figure 5.4: In both figures,
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
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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
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7.5 Build tree prediction The work
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Questions The question that we will
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Table 7.4 shows the full results, t
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that this average “missed” (unp
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7.6 Openings 7.6.1 Bayesian model W
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Questions The question that we will
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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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