Conditional planning has to work regardless of the outcome of an action.

It takes place in Fully Observable Environment where the current state of the agent is known environment is fully observable. The outcome of actions cannot be determined so the environment is said to be nondeterministic.

Here we can check what is happening in environment at predermined points of the plan to deal with ambiguous actions.

It needs to take some actions at every state and must be able to handle every outcome for the action it takes. A state node is represented with a square and chance node is represented with a circles.

For a state node we have an option of choosing some actions. For a chance node agent has to handle every outcome.

Conditional Planning can also take place in the Partially Observable Environments where, we cannot keep a track on every state.

In vacuum cleaner e.g. if the dirt is at Right and agent knows about Right, but not about Left. Then, in such cases Dirt might be left behind when the agent, leaves a clean square. Initial state is also called as a state set or a belief state.

Sensors play important role in Conditional planning for partially observable environments. Automatic sensing can be useful; with automatic sensing an agent gets all the available percepts at every step.