Additive Pattern Database Heuristics

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2.4 Statically-Partitioned Additivedatabase Heuristics●●●Disjoint pattern databases: partition the tilesinto disjoint groups, such that every tile isincluded in a group, and no tile belongs to morethan one groupsPrecompute pattern databases of the minimumnumber of moves of the the tiles in each groupRetrieve the number of moves required to solvethe tiles, Add together the values for eachgroup
Statically-Partitioned Additivedatabase Heuristics●●this value>=Manhattan distance, because itaccounts for interactions between tiles in thesame groupThe key difference between additive databasesand the non-additive databases is: non-additivedatabases include all moves required to solvethe pattern tiles, including moves of tiles not inthe pattern group. In an additive patterndatabase, we only count moves of the tiles inthe group.
Page 1 and 2: Additive Pattern Database Heuristic
Page 3 and 4: ●●The most effective way to imp
Page 5 and 6: ●●●An optimal solution to the
Page 7 and 8: 1.3 two main contributions of thisp
Page 9: 2. Previous Work on AdmissibleHeuri
Page 13 and 14: 3. Dynamically-Partitioned Database
Page 15 and 16: ●For each state of the search , t
Page 17 and 18: Dynamically-Partitioned databaseHeu
Page 19 and 20: 4.2.2 fifteen puzzle partition
Page 21 and 22: 4.2.3 Twenty-four puzzle
Page 23 and 24: 4.2.4 Thirty-five puzzle
Page 25 and 26: 4.2.5 Discussion of the tile puzzle
Page 27 and 28: Experimental results for the 15-dis
Page 29 and 30: 6. Vertex Cover
Page 32 and 33: 7. General Characterization of theM
Page 34 and 35: 7.2.2 Partial pattern databases●
Page 36 and 37: The steps for the staticallypartiti
Page 38 and 39: 7.4 Differences between theapplicat
Page 40 and 41: 8.2 Conclusions and Further Work●

Additive Pattern Database Heuristics

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