Fast Downward Stone Soup : A Baseline for Building Planner Portfolios
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Summary
A simple general method for concocting “planner soups”, sequential portfolios of planning algorithms, and the actual recipes used for Fast Downward Stone Soup in the sequential optimization and sequential satisficing are described.
- Published
- 2011-01-01
- Cited by
- 89
- References
- 21
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:13477963
References
- Sensible Agent Technology Improving Coordination and Communication in Biosurveillance Domains
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- Rog-O-Matic : a belligerent expert system
- 从学徒到大师之路--读《 The Pragmatic Programmer, From Journeyman to Master》
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- The More, the Merrier: Combining Heuristic Estimators for Satisficing Planning
- Flexible Abstraction Heuristics for Optimal Sequential Planning
- Planning as heuristic search
- Concise finite-domain representations for PDDL planning tasks
- A Planning Heuristic Based on Causal Graph Analysis
- The Fast Downward Planning System
- The LAMA Planner: Guiding Cost-Based Anytime Planning with Landmarks
- The Joy of Forgetting: Faster Anytime Search via Restarting
- Landmarks Revisited
- ParamILS: An Automatic Algorithm Configuration Framework
- ISAC - Instance-Specific Algorithm Configuration
- Heuristics for Planning with Action Costs Revisited
Cited by
- AI Planning-Based Service Modeling for the Internet of Things
- Challenges of Portfolio-based Planning
- Planning through Automatic Portfolio Configuration: The PbP Approach
- Automatic construction of optimal static sequential portfolios for AI planning and beyond
- Portfolio-based planning: State of the art, common practice and open challenges
- ASlib: A benchmark library for algorithm selection
- Using Alternative Suboptimality Bounds in Heuristic Search
- Constrained Symbolic Search: On Mutexes, BDD Minimization and More
- Latent Features for Algorithm Selection
- aspeed: Solver scheduling via answer set programming 1
- Adding Exploration to Greedy Best-First Search
- claspfolio 2: Advances in Algorithm Selection for Answer Set Programming
- Balancing Exploration and Exploitation in Classical Planning
- Online Speedup Learning for Optimal Planning
- Algorithm Selection, Scheduling and Con guration of Boolean Constraint Solvers
- Finding Better Candidate Algorithms for Portfolio-Based Planners
- Transition Trees for Cost-Optimal Symbolic Planning
- Automatic Configuration of Sequential Planning Portfolios
- Learning Portfolios of Automatically Tuned Planners
- What Language Do You Use to Create Your AI Programs and Why?
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