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

7.8 Conclusion
We contributed a probabilistic model to be able to compute the distribution over openings
(strategies) of the opponent in a RTS game from partial and noisy observations. The bot can
adapt to the opponent’s strategy as it predicts the opening with 63 − 68% of recognition rate
at 5 minutes and > 70% of recognition rate at 10 minutes (up to 94%), while having strong
robustness to noise (> 50% recognition rate with 50% missing observations). It can be used in
production due to its low CPU (and memory) footprint.
We also contributed a semi-supervised method to label RTS game logs (replays) with openings
(strategies). Both our implementations are free software and can be found online 10 . We use
this model in our StarCraft AI competition entry bot as it enables it to deal with the incomplete
knowledge gathered from scouting.
We presented a probabilistic model inferring the best army composition given what was
previously seen (from replays, or previous games), integrating adaptation to the opponent with
other constraints (tactics). One of the main advantages of this approach is to be able to deal
natively with incomplete information, due to player’s intentions, and to the fog of war in RTS.
The army composition dimensionality reduction (clustering) can be applied to any game and
coupled with other techniques, for instance for situation assessment in case-based planning.
The results in battle outcome prediction (from few information) shows its situation assessment
potential.
10 https://github.com/SnippyHolloW/OpeningTech/
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