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

Probabilistic modality
Finally, we could use multi-modality [Colas et al., 2010] to get rid of the remaining (small: fireretreat-move)
FSM. Instead of being in “hard” modes, the unit could be in a weighted sum of
modes (summing to one) and we would have:
P(Dir) = w fightMove()P(Dir|sensory_inputs) fightMove()+w flee()P(Dir|sensory_inputs) flee() . . .
This could particularly help dealing with the fact that parameters learned from fixed scenarii
would be too specialized. This way we could interpolate a continuous family of distributions for
P(Dir) from a fixed and finite number of parameters learned from a finite number of experiments
setups.
5.6.2 Conclusion
We have implemented this model in StarCraft, and it outperforms the original AI as well as
other bots. Our approach does not require a specific vertical integration for each different type
of objectives (higher level goals), as opposed to CBR and reactive planning [Ontañón et al.,
2007a, Weber et al., 2010b]: it can have a completely different model above feeding sensory
inputs like Obji. It runs in real-time on a laptop (Core 2 Duo) taking decisions for every units
24 times per second. It scales well with the number of units to control thanks to the absence of
communication at the unit level, and is more robust and maintainable than a FSM. Particularly,
the cost to add a new sensory input (a new effect on the units behavior) is low.
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