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B.3 Strategy B.4 BroodwarBotQ Algorithm 9 Flow algorithm making sure there are no convex closures function init(region) {diri,j ← list()|(i, j) ∈ region} updated ← {(i, j)|(i, j) ∈ entrance(region)} {diri,j ← dir_towards(region)|(i, j) ∈ updated} while ∃diri,j == list() do (sources, new_updated) ← neighbours_couples(updated) for all ((x, y), (i, j)) ∈ (sources, new_updated) do if (x − i, y − j) /∈ dirx,y then diri,j.append((i − x, j − y)) end if end for updated ← new_updated end while end function function build(i, j) refill ← list() for all (x, y) ∈ neighbours(i, j) do ⊲ cut the flow arround dirx,y.remove((x − i, y − j)) if dirx,y.empty() then refill.append((x, y)) build(x, y) ⊲ Recursively cut the flow end if end for while ¬fixed_point do ⊲ refill as in the initialization... current ← dir for all (x, y) ∈ refill do if ∃(i, j) ∈ neighbours(x, y) such that currenti,j �= list() then ⊲ non empty flow dirx,y.append((x − i, y − j)) end if end for end while end function function is_isolated?(i, j) if diri,j.empty() then return True else return False end if end function 204
C C A A C C A A 4 B B 4 3 4 B B 4 3 2 3 D D D 3 2 1 2 D D D 2 1 4 3 2 C C A A C C A A 4 3 B B 3 2 B B 2 1 D D D D D D Filling: 1, 2, 3, 4... Empyting: 1, 2, 3, 4 Figure B.7: Example of the algorithm 9 in a case in which there are buildings A, B, C, D and building in the new cross spots would close the paths to the interior and trap units. Left: flood filling of the region with the source at the choke (bottom right). The arrows show the dir vectors for each tile. Right: recursive calls to build which drain the interior zone. Refilling will not be able to refill the middle. Figure B.8: Bots ladder on February 12th, 2012. With BroodwarBotQ using a Bayesian model for opponent’s strategy prediction as well as for micro-management*. 205
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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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Finally, our approach is not exclus
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Chapter 7 Strategy Strategy without
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etween economy, technology and mili
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power to the player (it allows for
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7.4.2 Probabilistic labeling Instea
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• Variables: - X i∈�1...n�
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Figure 7.3: Protoss vs Terran distr
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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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Page 186 and 187: Damian Isla. Handling complexity in
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