Similar to the various relaxed plan heuristics for the multiple graphs, we can compute a max, sum, or level heuristic on each of the multiple planning graphs and aggregate them with a maximum or summation to respectively measure positive interaction or independence. The reason we cannot aggregate the individual graph heuristics to measure overlap is that they are numbers, not sets of actions. Measuring overlap involves taking the union of heuristics from each graph and the union of numbers is not meaningful like the union of action sets from relaxed plans. Like before, there is no reason to use multiple graphs if there is no state distance aggregation.
Positive Interaction Aggregation:
Note that this heuristic is admissible. By the same reasoning as in classical planning, the first level where all the subgoals are present and non-mutex is an underestimate of the true cost of a state. This holds for each of the graphs. Taking the maximum accounts for the most difficult world in which to achieve a constituent of and is thus a provable underestimate of . GPT's max heuristic  is similar to , but is computed with dynamic programming in state space rather than planning graphs.
Independence Aggregation: All heuristics mentioned for Positive Interaction Aggregation can be augmented to take the summation of costs found on the individual planning graphs rather than the maximum. We denote them as: , , and . None of these heuristics are admissible because the same action may be used in all worlds, but we count its cost for every world by using summation.