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Maximilian Fickert
Maximilian Fickert
Saarland University, Saarland Informatics Campus
Bestätigte E-Mail-Adresse bei cs.uni-saarland.de - Startseite
Titel
Zitiert von
Zitiert von
Jahr
Combining the delete relaxation with critical-path heuristics: A direct characterization
M Fickert, J Hoffmann, M Steinmetz
Journal of Artificial Intelligence Research 56, 269-327, 2016
152016
Complete local search: Boosting hill-climbing through online relaxation refinement
M Fickert, J Hoffmann
Twenty-Seventh International Conference on Automated Planning and Scheduling, 2017
142017
Saarplan: Combining saarland’s greatest planning techniques
M Fickert, D Gnad, P Speicher, J Hoffmann
IPC2018–Classical Tracks, 10-15, 2018
122018
Explicit Conjunctions without Compilation: Computing hFF (PiC) in Polynomial Time
J Hoffmann, M Fickert
Proceedings of the International Conference on Automated Planning and …, 2015
122015
A novel dual ascent algorithm for solving the min-cost flow problem
R Becker, M Fickert, A Karrenbauer
2016 Proceedings of the Eighteenth Workshop on Algorithm Engineering and …, 2016
92016
Making hill-climbing great again through online relaxation refinement and novelty pruning
M Fickert
International Symposium on Combinatorial Search 9 (1), 2018
82018
Beliefs we can believe in: Replacing assumptions with data in real-time search
M Fickert, T Gu, L Staut, W Ruml, J Hoffmann, M Petrik
Proceedings of the AAAI Conference on Artificial Intelligence 34 (06), 9827-9834, 2020
72020
A novel lookahead strategy for delete relaxation heuristics in greedy best-first search
M Fickert
Proceedings of the International Conference on Automated Planning and …, 2020
62020
Online refinement of Cartesian abstraction heuristics
R Eifler, M Fickert
Eleventh Annual Symposium on Combinatorial Search, 2018
62018
Ranking conjunctions for partial delete relaxation heuristics in planning
M Fickert, J Hoffmann
Tenth Annual Symposium on Combinatorial Search, 2017
52017
OLCFF: Online-learning hCFF
M Fickert, J Hoffmann
Ninth International Planning Competition (IPC-9): planner abstracts, 17-19, 2018
42018
Refining abstraction heuristics during real-time planning
R Eifler, M Fickert, J Hoffmann, W Ruml
Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 7578-7585, 2019
32019
Choosing the Initial State for Online Replanning
M Fickert, I Gavran, I Fedotov, J Hoffmann, R Majumdar, W Ruml
Proceedings of the AAAI Conference on Artificial Intelligence 35 (14), 12311 …, 2021
22021
Bounded-cost search using estimates of uncertainty
M Fickert, T Gu, W Ruml
Proceedings of IJCAI-21, 2021
22021
Real-time Planning as Data-driven Decision-making
M Fickert, T Gu, L Staut, S Lekyang, W Ruml, J Hoffmann, M Petrik
the ICAPS-20 Workshop on Bridging the Gap Between AI Planning and …, 2020
22020
Unchaining the Power of Partial Delete Relaxation, Part II: Finding Plans with Red-Black State Space Search.
M Fickert, D Gnad, J Hoffmann
IJCAI, 4750-4756, 2018
22018
Novel is not always better: On the relation between novelty and dominance pruning
J Groß, A Torralba, M Fickert
Proceedings of the AAAI Conference on Artificial Intelligence 34 (06), 9875-9882, 2020
12020
Simultaneous Re-Planning and Plan Execution for Online Job Arrival
I Gavran, M Fickert, I Fedotov, J Hoffmann, R Majumdar
HSDIP 2019, 55, 2019
12019
Explicit conjunctions w/o compilation: Computing hFF (πc) in polynomial time (technical report)
J Hoffmann, M Fickert
Technical report, Saarland University. Available at http://fai. cs. uni …, 2015
12015
Online Relaxation Refinement for Satisficing Planning: On Partial Delete Relaxation, Complete Hill-Climbing, and Novelty Pruning
M Fickert, J Hoffmann
Journal of Artificial Intelligence Research 73, 67–115, 2022
2022
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