State space search systems commonly represent actions as a pair of precondition and effect. The precondition is evaluated to check if the transition from the action is applicable, and the effect is applied to a copy of the current state to construct a successor. Even though this process has linear time complexity, there remains potential to optimize the implementation further. This thesis develops an automated planning implementation that compactly represents the state variables in 64-bit words and generate low level code for evaluating the preconditions and effects of actions from a multi-valued planning task. The compact state representation has logarithmic space complexity in the size of the variable domain. For each planning problem, low level code is generated to evaluate the preconditions and actions from that problem. The generated code is incorporated into an implementation of the Fast Forward heuristic and two systematic search algorithms: greedy best-first search and simulated annealing. The system is evaluated on a number of domain problems from planning competitions and compared with the performance of the Fast Downward planner. The results show good performance on the planning problems, especially during the successor generation phase, demonstrating the potential of the method as a viable path for optimizing state space search.
