Automated Planning

STRIPS was an automated planner developed at Stanford University and which paved the way for the beginnings of automated planning AI. Automated planning allows planners to make their way through an environment or a task using the same algorithm to solve completely different tasks. This can have many applications all over the world but is most commonly used for robotics.

               An automated planning algorithm needs to know certain information about the world it is functioning in to be able to plan correctly. The algorithm needs to be aware of actions that it is capable of doing. It can use these actions to choose what to do. It also needs to know what objects it is able to interact with. These objects can be chosen to be used with certain actions to be at certain states. Once the algorithm knows everything of everything around it, it can start planning for an optimal path to the goal.

Implementing these algorithms into robots is extremely helpful because it can teach robots to reach certain goals without needing a completely different algorithm to be coded for the environment it’s in. The robot just needs to know the actions that are doable in that environment and the objects it can interact with. For example, a robot working at a storage warehouse could be given the knowledge that there are boxes stacked around the warehouse and that they can be moved. If a box needs to be retrieved, the robot can go pick up the box and bring it to the destination. If the desired box is covered in a stack of other boxes, the robot must plan the best way to move the other boxes to get to the desired box. The robot will know the action of moving boxes and will know that there are boxes in the world that it can interact with.

               This same robot could then be reprogrammed to vastly different tasks. The actions and objects of this different domain would be taught to the robot. For example, the robot could have a knowledge of gardening. It could know the act of planting, watering, and weeding. It could then have a goal of always having a clean and watered garden bed. The same algorithm would be used to find the best way to plant all the seeds and watering them. The robot would also know of certain preconditions that would have to be fulfilled. For example; an area of dirt must have a seed in it for it to be watered. Or an area of dirt must have a weed in it for the robot to act in removing the weed. These algorithms can adapt to very different domains as long as they know the required objects and actions they will be dealing with.