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

• if allies (possibly in a list) are not “buffed” (enhanced), buff them if possible.
• default: shoot at the most targeted enemy.
• self-preservation, etc.
The problem with such an approach is that tuning or modifying it can quickly be cumbersome:
for a game with a large state space, robustness is hard to ensure. Also, the behavior would seem
pretty repetitive, and thus predictable.
Figure 3.2: Example setup A (left) and B (right). There are 2 foes and 2 allies taking damages
(“MT” and “tank”). Players with stars can heal allies, players with dotted lines will soon die
(ID = true). Our model controls the blue character, green players are allies, while red characters
are foes. The larger the surrounding ellipse is, the more health points the characters have.
3.3.2 Perceptions and actions
To solve this problem without having the downsides of this naive first approach, we will use
a Bayesian model. We now list some of the perceptions available to the player (and thus the
robot):
• the names and positions of all other players, this gives their distances, which is useful to
know which abilities can be used.
• hit points (also health points, noted HP) of all the players, when the HP of a player fall
to 0, her avatar dies. It cannot be resurrected, at least in the situations that we consider
(not for the fight).
• the natures of other players (ally or foe).
• the class of other players: some have a tanking role (take incoming damages for the rest
of the group), others are damage dealers (either by contact or ranged attacks), the last
category are healers, whose role is to maintain everyone alive.
• an approximation of the resistance to certain types of attacks for every player.
Actions available to the players are to move and to use their abilities:
• damage over time attacks (periodic repeated damage), or quick but low damage attacks,
and slow but high damage attacks
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