Artificial Intelligence
The qualitative process engine
Readings in qualitative reasoning about physical systems
An analysis of ATMS-based techniques for computing Dempster-Shafer belief functions
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Scaling up self-explanatory simulators polynomial time compilation
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Scaling up logic-based truth maintenance systems via fact garbage collection
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
| Hi-index | 0.00 |
Assumption-based truth maintenance systems have developed into powerful and popular means for considering multiple contexts simultaneously during problem solving. Unfortunately, increasing problem complexity can lead to explosive growth of node labels. In this paper, we present a new ATMS algorithm (CATMS) which avoids the problem of label explosions, while preserving most of the querytime efficiencies resulting from label compilations. CATMS generalizes the standard ATMS subsumption relation, allowing it to compress an entire label into a single assumption. This compression of labels is balanced by an expansion of environments to include any implied assumptions. The result is a new dimension of flexibility, allowing CATMS to trade-off the query-time efficiency of uncompressed labels against the costs of computing them. To demonstrate the significant computational gains of CATMS over de Kleer's ATMS, we compare the performance of the ATMS-based QPE [9] problem-solver using each.