Probabilistic Planning is Multi-objective!

Probabilistic planning is an inherently multi-objective problem where plans must trade-off probability of goal satisfaction with expected plan cost. To date, probabilistic plan synthesis algorithms have focussed on single objective formulations that bound one of the objectives by making some unnatural assumptions. We show that a multi-objective formulation is not only needed, but also enables us to (i) generate Pareto sets of plans, (ii) use recent advances in probabilistic planning reachability heuristics, and (iii) elegantly solve limited contingency planning problems. We extend LAO∗ to its multi-objective counterpart MOLAO∗, and discuss a number of speed-up techniques that form the basis for a state of the art conditional probabilistic planner.