Additive Pattern Database Heuristics

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1.2 Subgoal Interactions●●●Inaccuracy of the Manhattan distance: itassumes that each tile can be moved along ashortest path to its goal location withoutinterference from any other tilesThe tiles do interfere with each otherThe key to developing a more accurateheuristic function is to account for some ofthose interactions in the heuristic
1.3 two main contributions of thispaper●●Show that additive pattern databases whichwere first developed and implemented for thesliding tile puzzles can be generalized andapplied to other domains.Divide additive pattern databases into twomethods, statically- and dynamically-partitioneddatabases, and compare the two methods
Page 1 and 2: Additive Pattern Database Heuristic
Page 3 and 4: ●●The most effective way to imp
Page 5: ●●●An optimal solution to the
Page 9 and 10: 2. Previous Work on AdmissibleHeuri
Page 11 and 12: Statically-Partitioned Additivedata
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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