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

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• Complexity: pspace-complete [Papadimitriou <strong>and</strong> Tsitsiklis, 1987, Viglietta, 2012]. Our<br />

solutions are real-time on a laptop.<br />

Opponent Strategy<br />

Incomplete<br />

Data<br />

Our Strategy Our Tactics<br />

Unit UnitGroups<br />

Groups<br />

Production planner<br />

<strong>and</strong> managers<br />

Opponent Tactics Opponent Positions<br />

Our Style<br />

(+ meta)<br />

<strong>Bayesian</strong>Unit<br />

<strong>Bayesian</strong>Unit<br />

<strong>Bayesian</strong>Unit<br />

<strong>Bayesian</strong>Unit<br />

<strong>Bayesian</strong>Unit<br />

<strong>Bayesian</strong>Unit<br />

<strong>Bayesian</strong>Unit<br />

<strong>Bayesian</strong>Unit<br />

Figure 5.1: In<strong>for</strong>mation-centric view of the architecture of the bot, the part concerning this<br />

chapter (micro-management*) is in the dotted rectangle<br />

5.1 Units management<br />

5.1.1 Micro-management complexity<br />

In this part, we focus on micro-management*, which is the lower-level in our hierarchy of decisionmaking<br />

levels (see Fig. 4.5) <strong>and</strong> directly affect units control/inputs. Micro-management consists<br />

in maximizing the effectiveness of the units i.e. the damages given/damages received ratio.<br />

These has to be per<strong>for</strong>med simultaneously <strong>for</strong> units of different types, in complex battles, <strong>and</strong><br />

possibly on heterogeneous terrain. For instance: retreat <strong>and</strong> save a wounded unit so that the<br />

enemy units would have to chase it either boosts your firepower (if you save the unit) or weakens<br />

the opponent’s (if they chase).<br />

StarCraft micro-management involves ground, flying, ranged, contact, static, moving (at<br />

different speeds), small <strong>and</strong> big units (see Figure 5.2). Units may also have splash damage,<br />

spells, <strong>and</strong> different types of damages whose amount will depend on the target size. It yields<br />

a rich states space <strong>and</strong> needs control to be very fast: human progamers can per<strong>for</strong>m up to<br />

400 “actions per minute” in intense fights. The problem <strong>for</strong> them is to know which actions are<br />

effective <strong>and</strong> the most rewarding to spend their actions efficiently. A robot does not have such<br />

physical limitations, but yet, badly chosen actions have negative influence on the issue of fights.<br />

Let U be the set of the m units to control, A = {∪i � di} ∪ S be the set of possible actions<br />

(all n possible � d directions, st<strong>and</strong>ing ground included, <strong>and</strong> Skills, firing included), <strong>and</strong> E the<br />

<strong>for</strong> allied units is known, the disadvantage is that the combinatorics of T <strong>and</strong> A make it intractable <strong>for</strong> useful<br />

problems.<br />

70

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