Future Directions in Heuristic Search

6 Conclusion

This work aimed to evaluate the performance of several known heuristic search algorithms, such as D*, D* Lite, LPA*, LRTA*, RTAA*, and ARA* in terms of solving time, memory consumption, and path cost. The evaluation was made using different randomly generated grid environments with different characteristics alongside personalized grid environments with horizontal walls and different wall lengths as different hinders.

Our experimental evaluation revealed that all the algorithms exhibit different performances with strengths and weaknesses under different grid characterizations. D* Lite consistently generated the shortest paths even in obstacle-dense grids, indicating its efficiency in dense environments. ARA* consistently provides faster solutions as the grid size increases, particularly larger than 100, while RTTA* generates faster solutions in smaller grid sizes, with the advantage of not being affected by dense environments.

Our study provides valuable insights into selecting the appropriate heuristic search algorithm in the pathfinding domain. Using these insights, we propose a selection algorithm used to optimize the performance needed in a pathfinding domain, such as, solving time, path length, or memory consumption. However, our evaluation focused only on static environments, while dynamic environments may introduce additional challenges. Furthermore, we considered a limited set of experiments, not covering all possible combinations of the grid characteristics. This limitation means that the selection algorithm might not always recommend the most optimal solution, as seen in the example we introduced to evaluate our algorithm.

In future work, we aim to address these limitations as follows: Firstly, we plan to extend our evaluation of the search algorithms to include dynamic environments. Secondly, we intend to explore various combinations of both domain characterization and priorities. Furthermore, we want to include additional scenario configuration, extending the random obstacles and the walls scenarios. With such additional understanding, we aim to refine our selection algorithm to automatically take decisions among the best search algorithms, based on the type of obstacles in the local search space.

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