Daniel Fišer, Rostislav Horčík, Antonín Komenda |
PosterID:
56
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| Potential heuristics assign a numerical value (potential) to each fact and compute the heuristic value for a given state as a sum of these potentials. Mutexes are invariants stating that a certain set of facts cannot be part of any reachable state. In this paper, we use mutexes to improve potential heuristics in two ways. First, we show that the mutex-based disambiguations of the goal and preconditions of operators leads to a less constrained linear program providing a better set of potentials. Second, we utilize mutexes in a construction of new optimization functions based on counting of a number of states containing certain sets of facts. The experimental evaluation shows a significant increase in the number of solved tasks. | |
| Canb | 10/28/2020, 21:00 – 21:45 |
| 10/30/2020, 04:00 – 04:45 |
| Paris | 10/28/2020, 11:00 – 11:45 |
| 10/29/2020, 18:00 – 18:45 |
| NYC | 10/28/2020, 06:00 – 06:45 |
| 10/29/2020, 13:00 – 13:45 |
| LA | 10/28/2020, 03:00 – 03:45 |
| 10/29/2020, 10:00 – 10:45 |
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Daniel Fišer, Rostislav Horčík, Antonín Komenda