FIGURE 1. Example setup A (left) and B (right), 2 foes, 2 "tanks", players with stars can heal allies, 

![0_image_0.png](0_image_0.png)

players with dotted lines will soon die (*ID=true*).

This model has been applied to a simulated situation with 2 foes and 4 allies while our robot took the part of a "druid", a versatile class that can cast spells to do direct damages, damages over time, buff (enhancements), debuff, crowd-control, heal and heal over time. We display a schema of this situation in Figure 1. The arrows indicate foes attacks on allies. The larger the ring is, the more health points the characters have. MT stands for "main tank", Add for "additional foe". We worked with the skills corresponding to a Druid. HOT stands for heal over time, DOT for "damage over time", "abol" for abolition and "regen" for regeneration, a buff is an enhancement and a "dd" is a direct damage. "Root" is a spell which disables the target to move for a short period of time, useful to flee or to put some distance between the enemy and the druid to cast attack spells. "Small" spells are usually faster to cast than "big" spells.

Skills ∈ { small_heal, big_heal, HOT, poison_abol, malediction_abol, buff_armor, regen_mana, small_dd, big_dd, DOT, debuff_armor, root }
We did not do the "Identification" part, which consists in learning the probability tables from observations. To keep things simple and because we wanted to explore the model, we worked with manually defined probability tables. So we introduced "soft evidences", parameters that will modify the conditional probability tables, which we will change to watch their effects. For instance the "soft evidence that a selected target is foe" and the "soft evidence that a selected target will soon die *(ID=true)*" that will consequently modify the probability tables of *P(Ai)* and *P(IDi)* respectively. We set the probability to target the same target as before to 0.4 and the previous target to *Lich* so that the prior probability for all other 6 targets is 0.1 (4 times more chances to target the *Lich* than any other character). We set the soft evidence *P(Ai=false|T=i)* to 0.6. 

This means that our robotic *Druid* is mainly a damage dealer and not a healer. For the "target selection" model, we can see on Figure 2.A that the evolution from selecting the main foe *Lich* to selecting the ally *Tank* is driven by the increase of "soft evidence that a selected target will soon die" and our robot eventually moves on targeting his Tank ally (to heal him). We can see on Figure 2.B that, at some point, the robotic Druid prefers to kill the dying Add to save his ally *Tank* instead of healing him. Note that there is no variable showing the relation between Add and *Tank* (the first is attacking the second, who is taking damages from the first), but this is under consideration for a future, more complete, model.