The LAMA Planner: Guiding Cost-Based Anytime Planning with Landmarks

DOI10.1613/jair.2972zbMath1205.68383arXiv1401.3839OpenAlexW3106271432MaRDI QIDQ3055802

Silvia Richter, Matthias Westphal

Publication date: 10 November 2010

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

Full work available at URL: https://arxiv.org/abs/1401.3839

zbMATH Keywords

landmarkssatisficing planning

Mathematics Subject Classification ID

Searching and sorting (68P10) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)

Related Items (17)

Extracting mutual exclusion invariants from lifted temporal planning domains ⋮ Planning as satisfiability: heuristics ⋮ The MADLA planner: multi-agent planning by combination of distributed and local heuristic search ⋮ Planning with Critical Section Macros: Theory and Practice ⋮ Automatic construction of optimal static sequential portfolios for AI planning and beyond ⋮ Agent planning programs ⋮ Unnamed Item ⋮ Cost-optimal Planning, Delete Relaxation, Approximability, and Heuristics ⋮ Qualitative constraint satisfaction problems: an extended framework with landmarks ⋮ Selecting goals in oversubscription planning using relaxed plans ⋮ State space search nogood learning: online refinement of critical-path dead-end detectors in planning ⋮ Star-topology decoupled state space search ⋮ Landmark-based approaches for goal recognition as planning ⋮ Computing programs for generalized planning using a classical planner ⋮ Compact and efficient encodings for planning in factored state and action spaces with learned binarized neural network transition models ⋮ Red-black planning: a new systematic approach to partial delete relaxation ⋮ Online Relaxation Refinement for Satisficing Planning: On Partial Delete Relaxation, Complete Hill-Climbing, and Novelty Pruning

This page was built for publication: The LAMA Planner: Guiding Cost-Based Anytime Planning with Landmarks