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.
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