After some careful considerations I have reviewed my high level design. I think I was looking at things the wrong way. Instead of the Bayesian network classifying actions to take given events, that will be the neural networks job. the Bayesian network's purpose is to recognize events.
Events will trigger queries to compute the probability of actions a bot might take, and they will be fed to the neural net, alongside botstate data from the bot using the neural net to make decisions, and each output neurons of the neural network should classify the best action to take.
This approach should make taking training data from the game engine a lot easier, however in order to get the neural net working the way it should I need to have a fully functional Bayesian Net.
The actions and variables I've decided to compute the probability for in the Bayesian net are as follows:
Probable Actions:
Seeking Health
Seeking Armor
Seeking Ammo
Seeking Enemy
Running Away
Variables
Health
Ammo
Armor
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