@@ -60,9 +60,8 @@ public List<List<ActionSchema>> graphPlan(Problem problem) {
6060 return solution ;
6161 }
6262 // if graph and nogoods have both leveled off then return failure
63- if (levelledOff (graph ) && leveledOff (nogoods )) {
63+ if (levelledOff (graph ) && leveledOff (nogoods ))
6464 return null ;
65- }
6665 // graph ← EXPAND-GRAPH(graph, problem)
6766 graph = expandGraph (graph );
6867 }
@@ -85,11 +84,12 @@ public List<List<ActionSchema>> graphPlan(Problem problem) {
8584 * of their preconditions are mutex.
8685 * • The goal is to reach a state at level S 0 such that all the goals are satisfied.
8786 * • The cost of each action is 1.
87+ *
8888 * Here a simple depth-first search is used.
8989 *
9090 * @param graph The planning graph.
9191 * @param goals Goals of the planning problem.
92- * @param numLevel Number of levels in the graph.
92+ * @param level Number of levels in the graph.
9393 * @param nogoods A hash table to store previously calculated results.
9494 * @return a solution if found else null
9595 */
@@ -110,10 +110,8 @@ private List<List<ActionSchema>> extractSolution(Graph graph, List<Literal> goal
110110 setOfPossibleActions .add (possibleActionsPerLiteral );
111111 }
112112 List <List <ActionSchema >> allPossibleSubSets = generateCombinations (setOfPossibleActions );
113- boolean validSet ;
114- List <ActionSchema > setToBeTaken = null ;
115113 for (List <ActionSchema > possibleSet : allPossibleSubSets ) {
116- validSet = true ;
114+ boolean validSet = true ;
117115 ActionSchema firstAction , secondAction ;
118116 for (int i = 0 ; i < possibleSet .size (); i ++) {
119117 firstAction = possibleSet .get (i );
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