YE
Y. Elbakri
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Currently explanations for the Not-First/Not-Last propagators for the disjunctive constraint have not been explored thoroughly, and have room for improvement.
In this paper, we look into attempting to give more general explanations by looking through the powerset of tasks and finding a smaller set which still propagates.
We have implemented naive, the state-of-the-art and our subset-finding explanations and compared them all.
Experimentally, around 50\% of the results show a lower amount of conflicts and average literal bound distance, however a majority have a higher runtime.
In addition, the more subsets we consider the bigger the runtime, however it not necessarily decrease in amount of conflicts and average literal bound distance.
...
In this paper, we look into attempting to give more general explanations by looking through the powerset of tasks and finding a smaller set which still propagates.
We have implemented naive, the state-of-the-art and our subset-finding explanations and compared them all.
Experimentally, around 50\% of the results show a lower amount of conflicts and average literal bound distance, however a majority have a higher runtime.
In addition, the more subsets we consider the bigger the runtime, however it not necessarily decrease in amount of conflicts and average literal bound distance.
...
Currently explanations for the Not-First/Not-Last propagators for the disjunctive constraint have not been explored thoroughly, and have room for improvement.
In this paper, we look into attempting to give more general explanations by looking through the powerset of tasks and finding a smaller set which still propagates.
We have implemented naive, the state-of-the-art and our subset-finding explanations and compared them all.
Experimentally, around 50\% of the results show a lower amount of conflicts and average literal bound distance, however a majority have a higher runtime.
In addition, the more subsets we consider the bigger the runtime, however it not necessarily decrease in amount of conflicts and average literal bound distance.
In this paper, we look into attempting to give more general explanations by looking through the powerset of tasks and finding a smaller set which still propagates.
We have implemented naive, the state-of-the-art and our subset-finding explanations and compared them all.
Experimentally, around 50\% of the results show a lower amount of conflicts and average literal bound distance, however a majority have a higher runtime.
In addition, the more subsets we consider the bigger the runtime, however it not necessarily decrease in amount of conflicts and average literal bound distance.