More explanation clarification

README.md

@@ -30,7 +30,7 @@ The preconditions line contains states that the world must be in before the acti
 Note that the app follows a closed-world assumption; that is, any facts about the world not declared in the initial state are considered to be false. The parser is clever enough that you don't need to declare all your literals in the initial state, as long as you use them somewhere in the problem description. You can declare negations by using an exclamation mark (!) immediately before the name of a condition.
 
 ## Linear Solver
-The STRIPS planner is a simple linear solver. In planning literature, this is sometimes called a total order solver since it assumes all steps can be planned one after the other with a strict ordering. It works backwards by starting at the goal state and searching for a grounded action (one with all variables replaced with literals) that will satisfy a precondition of the goal state. When it finds an acceptable action, it adds it to the plan and the newly added action becomes the new goal state. If an action is added that conflicts with another already-satisfied goal, then the clobbered goal is reintroduced and resatisfied. The algorithm keeps working backwards, stacking subgoals on top of each other via a depth-first search until it either arrives at the goal state or exhausts all possible plans and gives up.
+The STRIPS planner is a simple linear solver. In planning literature, this is sometimes called a total order solver since it assumes all steps can be planned one after the other with a strict ordering. It works backwards by starting at the goal state and searching for a grounded action (one with all variables replaced with literals) that will satisfy a precondition of the goal state. When it finds an acceptable action, it adds it to the plan and the newly added action becomes the new goal state. If an action is added that conflicts with another already-satisfied goal, then the clobbered goal is reintroduced and resatisfied. The algorithm keeps working backwards, stacking subgoals on top of each other via a depth-first search until it either satisfies all the goals or exhausts all possible plans and gives up.
 
 ## Running the example
 To run the example problem, simply execute the following: