Another approach is to use Monte Carlo Tree Search (MCTS), which is a search algorithm commonly used in games with uncertain outcomes. MCTS samples possible future states and performs simulations to estimate the value of different actions and plans. This approach gives planners the ability to handle uncertainty and make informed decisions.

Additionally, some planning algorithms make use of heuristics that take uncertainty into account. For example, the MAXQ framework allows for specifying and reasoning about probabilistic subgoals, which can guide the planning process under uncertainty.

One way to handle uncertainty in planning is by using probabilistic planning algorithms, such as Partially Observable Markov Decision Processes (POMDPs) or Markov Decision Processes (MDPs) with stochastic state transitions and rewards. These algorithms can model and reason about the uncertainty in the system by assigning probabilities to different outcomes and actions.

Overall, incorporating uncertainty into planning algorithms is an active area of research, and different approaches exist depending on the specific problem domains and requirements.