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|Title:||Critical-Path Dead-End Detection versus NoGoods : Offline Equivalence and Online Learning|
Smith, Stephen F.
|Title:||Proceedings of the Twenty-Seventh International Conference on Automated Planning and Scheduling|
|Year of Publication:||2017|
|Title of the Conference:||ICAPS 2017|
|Place of the conference:||Pittsburgh, Pennsylvania USA|
|Publikation type:||Conference Paper|
|Abstract:||One traditional use of critical-path heuristic functions is as effective sufficient criteria for unsolvability. To employ this for dead-end detection, the heuristic function must be evaluated on every new state to be tested, incurring a substantial runtime overhead. We show herein that the exact same dead-end detector can be captured through a nogood, a formula phiOFF computed once prior to search. This is mostly of theoretical interest, as phiOFF is large. We obtain practical variants by instead incrementally generating a stronger nogood psi, that implies phiOFF, online during search, generalizing from already tested states to avoid future heuristic-function evaluations.|
|URL of the first publication:||https://aaai.org/ocs/index.php/ICAPS/ICAPS17/paper/view/15642|
|Link to this record:||hdl:20.500.11880/28337|
|Date of registration:||21-Nov-2019|
|Third-party funds sponsorship:||DFG “Critically Constrained Planning via Partial Delete Relaxation"; BMBF through funding for the Center for IT-Security, Privacy and Accountability (CISPA)|
|Sponsorship ID:||DFG HO 2169/5-1; BMBF 16KIS0656|
|Faculty:||MI - Fakultät für Mathematik und Informatik|
|Department:||MI - Informatik|
|Professorship:||MI - Prof. Dr. Jörg Hoffmann|
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