Online Relaxation Refinement for Satisficing Planning: On Partial Delete Relaxation, Complete Hill-Climbing, and Novelty Pruning

DOI10.1613/jair.1.13153OpenAlexW4206232076MaRDI QIDQ5026253

Maximilian Fickert, Jörg Hoffmann

Publication date: 7 February 2022

Published in: Journal of Artificial Intelligence Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1613/jair.1.13153

zbMATH Keywords

heuristicsplanningsearch

Mathematics Subject Classification ID

Artificial intelligence (68Txx)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Learning heuristic functions for large state spaces
• State space search nogood learning: online refinement of critical-path dead-end detectors in planning
• Optimal admissible composition of abstraction heuristics
• Star-topology decoupled state space search
• Red-black planning: a new systematic approach to partial delete relaxation
• STRIPS: A new approach to the application of theorem proving to problem solving
• Real-time heuristic search
• Online Speedup Learning for Optimal Planning
• Effective Heuristics for Suboptimal Best-First Search
• The LAMA Planner: Guiding Cost-Based Anytime Planning with Landmarks
• Merge-and-Shrink Abstraction
• Counterexample-guided abstraction refinement for symbolic model checking
• Saturated Cost Partitioning for Optimal Classical Planning
• Improving Delete Relaxation Heuristics Through Explicitly Represented Conjunctions
• Combining the Delete Relaxation with Critical-Path Heuristics: A Direct Characterization
• Planning as heuristic search