planning with uncertainty decision networks the decision network code builds on the representation for belief networks of we first allow for factors that define the utility here the utility is function of the variables in vars in utility table the utility is defined in terms of aa list that enumerates the values as in section decnnetworks py -representations for decision networks from probgraphicalmodels import graphicalmodelbeliefnetwork from probfactors import factorcpdtabfactorfactor_timesprob from probvariables import variable import matplotlib pyplot as plt class utility(factor)""" factor defining utility""pass class utilitytable(tabfactorutility)""" factor defining utility using table""def __init__(selfvarstableposition=none)"""creates factor on vars from the table the table is ordered according to vars ""tabfactor __init__(self,vars,tableself position position decision variable is like random variable with string nameand domainwhich is list of possible values the decision variable also includes the parentsa list of the variables whose value will be known when the decision is made it also includes potionwhich is only used for plotting