In partial order planning , rather than searching over possible situation it involves searching over the space of possible plans. The idea is to construct a plan piece by piece.
Partial order planning is a method used in artificial intelligence for planning tasks where the order of actions is not strictly specified. In this approach, actions are executed based on their dependencies and preconditions rather than following a strict sequential order.
Here’s how it works:
- Action Representation: Actions are represented along with their preconditions (conditions that must be true before the action can be executed) and effects (changes that occur after the action is executed).
- Constraints and Dependencies: Actions may have temporal constraints or dependencies on other actions. These dependencies specify the order in which actions can be executed.
- Search and Optimization: Partial order planners use search algorithms to explore possible action sequences while respecting the constraints and dependencies. The goal is to find a plan that satisfies all preconditions and achieves the desired outcome.
- Plan Refinement: As the planner progresses, it may refine the plan by adding or removing actions based on the current state of the world and the available actions.
- Execution: Once a valid plan is generated, it can be executed to achieve the desired goal, taking into account any uncertainties or changes in the environment.
In summary, partial order planning involves representing actions and their dependencies, searching for a valid plan, and refining the plan as needed to achieve the desired outcome. It allows for flexibility in action sequencing, making it suitable for domains where strict ordering of actions is not feasible or desirable.