What is the purpose of Bayesian networks?
I am trying to state a possible bayes net:
What the creature wants to know is what to do next. So the root node is 'ACTION'. Possible actions are (let's say field 1 = home): [move to field 1, move to field 2, ... move to field n, sleep, eat] or [go north, go south, go east, go west, eat, sleep]. 'ACTION' depends on the creature's 'STATE', that can be [afraid(by another creature), under attack, hungry, tired]. 'STATE' depends on 'ANOTHER CREATURE' and 'FOOD AVAILABLE'. The latter two nodes can be [yes,no] and both depend on the current 'FIELD', [field 1, ..., field n].

'FIELD' -&gt; 'ANOTHER CREATURE'

'FIELD' -&gt; 'FOOD AVAILABLE'

'ANOTHER CRATURE' -&gt; 'STATE'

'FOOD AVAILABLE' -&gt; 'STATE'

'FIELD' -&gt; 'ACTION'

'STATE' -&gt; 'ACTION'


Each node has a table containing the probabilities p(value|value of dependency1, value dependency2,...). For example the 'ACTION' table holds the probabilities for each combination of p(action to take | value of 'STATE', value of 'FIELD').

I think suitable methods to estimate the model's probabilities are Q-learning or SARSA-learning.