

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

setup A | **FIGURE 2.B.** (right) Same, with setup B
For the "skill selection" model, we can see on Figure 3 the influence of IDi on Skill which is coherent with the Target distribution: either, in setup A, we evolve with the increase of *P(IDi=true|Target=i)* to choose to heal our ally or, in setup B, to deal direct damage (and hopefully, kill) the foe attacking him. As you can see here, when we have the highest probability to attack the main enemy ("Lich", when P(IDi=true|Target=i) is low), who is a *C=tank*, we get a high probability for the Skill debuff_armor. We only cast this skill if the debuff is not already present, so perhaps that we will cast *small_dd* instead. To conclude this example, Figure 4 shows the distribution on *P(T,S|all_status_variables)* with setup A and a the probability to target the previous target (set to *Lich* here) only ~2 times greater than any other character (so that we focus less on the same character), soft evidences *P(IDi=true|Target=i)=0.9* and *P(Ai=false|Target=i)=0.6*. In a greedy way, if the first couple (*T,S)* is already done or not available, we take the second.

![0_image_1.png](0_image_1.png)

## Discussion Perspectives

These results are encouraging, however the most challenging aspect will be to complete the model for different kind of tasks and for the robot to be able to learn sequences of actions, by developing forecasting strategy. This model has to be applied in a real MMORPG, out of its simulation, to reveal all its shortcomings and be improved. We foresee some future difficulties, for instance there is a possibility for many games that the Skill variable will be very big and that it will imply a too high computational cost. For that concern, we propose to cluster the skills in global skills (GS). This approach to break down the complexity of computation is *general* and can be used with other variables. 