Nathan R. Sturtevant, Shahaf Shperberg, Ariel Felner, Jingwei Chen |
PosterID:
34
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| The question of when bidirectional heuristic search outperforms unidirectional heuristic search has been revisited numerous times in the field of Artificial Intelligence. But, existing work addressing this question was published before the theory of bidirectional search was fully developed. This paper re-addresses the question of when bidirectional search outperforms unidirectional search using an updated theoretical understanding of the problem. We show that a core set of critical states in the state space are the primary factor determining whether a bidirectional search can outperform a unidirectional search and provide simple measures to determine whether a state space and heuristic contains these critical states. We similarly discuss and show the impact that asymmetry in the underlying problem graph has on whether a problem instance is bidirectional. | |
| Canb | 10/28/2020, 10:00 – 11:00 |
| 10/30/2020, 03:00 – 04:00 |
| Paris | 10/28/2020, 00:00 – 01:00 |
| 10/29/2020, 17:00 – 18:00 |
| NYC | 10/27/2020, 19:00 – 20:00 |
| 10/29/2020, 12:00 – 13:00 |
| LA | 10/27/2020, 16:00 – 17:00 |
| 10/29/2020, 09:00 – 10:00 |
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Predicting the Effectiveness of Bidirectional Heuristic Search
Nathan R. Sturtevant, Shahaf Shperberg, Ariel Felner, Jingwei Chen
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