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

and P(Ai = false|T = i) = 0.6. To make decisions about our actions, a simple approach would
be a greedy one: if the first couple (T, S) is already done or not available, we perform the second,
and so on. Another approach would be to sample in this distribution.
Figure 3.5: Log-probabilities of target (T ) and skill (S) with setup A, and P(IDi = true|T =
i) = 0.9, P(Ai = false|T = i) = 0.6. This plot corresponds to the answer to the full question
on which decision-making has to be done. The first choice (most probable P(S, T )) is to cast a
“big_heal” on the “tank”. The second choice is to cast “debuff_armor” on the “lich”...
3.3.5 Discussion
This limited model served the purpose of presenting Bayesian programming in practice. While
it was used in a simulation, it showcases the approach one can take to break down the problem
of autonomous control of NPC*. The choice of the skill or ability to use and the target on which
to use it puts hard constraints on every others decisions the autonomous agent has to take to
perform its ability action. Thus, such a model shows that:
• cooperative behavior is not too hard to incorporate in a decision (instead of being hardcoded),
• it can be learned, either from observations of a human player or by reinforcement (exploration),
• it is computationally tractable (for use in all games).
Moreover, using this model on another agent than the one controlled by the AI can give a
prediction on what it will do, resulting in human-like, adaptive, playing style.
55