Fast Planning Through Planning Graph Analysis

@inproceedings{Blum1995FastPT,
  title={Fast Planning Through Planning Graph Analysis},
  author={Avrim Blum and Merrick L. Furst},
  booktitle={IJCAI},
  year={1995}
}
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2,286 Citations
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Domain-independent temporal planning in a planning-graph-based approach
TLDR
A temporal planning approach that combines the principles of Graphplan and TGP, and uses the information calculated in the planning graph to deal with a model of actions that include local conditions and effects is presented.
Incremental Local Search for Planning Problems
TLDR
A new approach to planning in STRIPS-like domains based on an incremental local search process as an attempt to combine the advantages of a graph-based analysis and a partial-order planner is introduced.
On the application of least-commitment and heuristic search in temporal planning
TLDR
A new search process for temporal planning that uses the information of a planning graph and shows beneficial in the scalability of the planner and is competitive with other state-of-the-art planners w.r.t. the plan quality.
A Graph-based Approach for POCL Planning
TLDR
The goal of this paper is to show that the effort needed by of a partial-order planner (POP) to solve a problem can be dramatically reduced by properly exploiting the problem knowledge.
A Novel Constraint Model for Parallel Planning
TLDR
This paper proposes a novel view of constraint-based planning that uses parallel plans and multi-valued state variables based on the idea of timelines and their synchronization.
FIP: A Fast Planning-Graph-Based Iterative Planner
TLDR
Experimental results on several nondeterministic planning problems show that FIP is more efficient than the well-known planner, MBP, especially as the size of the problems increases.
Goal-Directed Flexible Graphplan
TLDR
A novel intelligent planning algorithm which expands the flexible planning graph backwards from the goal set and searches a valid plan forwards and proposes a new approach of backward flexible mutex inference, avoids a complicated process of satisfaction degree propagation, and grants to acquire the optimal plan.
Extracting Effective and Admissible State Space Heuristics from the Planning Graph
TLDR
The planning graph structure that Graphplan builds in polynomial time, provides a rich substrate for deriving more effective heuristics for state space planners, and the mutex information in the planning graph captures exactly this interaction information.
4SP: A four stage incremental planning approach
TLDR
A new approach for planning as an attempt to combine the advantanges of a graph-based analysis and a partialorder planner is shown: it takes a bit longer to solve some easy problems but it is capable to easily solve large problems.
A Planning Algorithm not based on Directional Search
TLDR
A simple planning algorithm is discussed that reconstructs the planner in the background of the SAT/CSP approach to solve general-purpose satisfiability problems in STRIPS planning.
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References

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