![]() The other 3 Operators follow similar logic, and this part is the cornerstone of Goal Stack Planning. BLOCK WORLD PROBLEM IN AI EXAMPLE FREEOn the other hand, once the operation is performed, The robot arm will be free ( ARMEMPTY) and the block X will be on top of Y ( ON(X,Y)). (Note : It is not necessary for the Precondition and DELETE List to be the exact same). Once the operation is performed, these predicates will cease to be true, thus they are included in DELETE List as well. to Stack Block X on top of Block Y, No other block should be on top of Y (CLEAR(Y)) and the Robot Arm should be holding the Block X ( HOLDING(X)). Initial State - ON(B,A) ∧ ONTABLE(A) ∧ ONTABLE(C) ∧ ONTABLE(D) ∧ CLEAR(B) ∧ CLEAR(C) ∧ CLEAR(D) ∧ ARMEMPTYįor example, to perform the STACK(X,Y) operation i.e. Using these predicates, we represent the Initial State and the Goal State in our example like this: Given below are the list of predicates as well as their intended meaning ![]() Predicates can be thought of as a statement which helps us convey the information about a configuration in Blocks World. Representing the configurations as a list of “predicates” World State on the other hand starts off as the Initial State and ends up being transformed into the Goal state.Īt the end of this algorithm we are left with an empty stack and a set of actions which helps us navigate from the Initial State to the World State. Goal Stack uses this world state to work its way from Goal State to Initial State. We make use of a stack to hold these goals that need to be fulfilled as well the actions that we need to perform for the same.Īpart from the “Initial State” and the “Goal State”, we maintain a “World State” configuration as well. We keep solving these “goals” and “sub-goals” until we finally arrive at the Initial State. These preconditions in turn have their own set of preconditions, which are required to be satisfied first. We start at the goal state and we try fulfilling the preconditions required to achieve the initial state. Goal Stack Planning is one of the earliest methods in artificial intelligence in which we work backwards from the goal state to the initial state. Our aim is to change the configuration of the blocks from the Initial State to the Goal State, both of which have been specified in the diagram above. The robot arm can move only one block at a time, and no other block should be stacked on top of the block which is to be moved by the robot arm. We have a robot arm to pick up or put down the blocks. Some blocks may or may not be stacked on other blocks. ![]() This is how the problem goes - There is a table on which some blocks are placed. In this medium article, we will take look at the Blocks World Problem and implement Goal Stack Planning to solve the same. ![]() Blocks World Problem - Initial State and Goal State for this article ![]()
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