EXCALIBUR
Adaptive Constraint-Based Agents in Artificial Environments

[REFINEMENT SEARCH]   [Total-Order Planning]   [Partial-Order Planning]   [Hierarchical Planning]   [Maximal Graphs]

[ Please note: The project has been discontinued as of May 31, 2005 and is superseded by the projects of the ii Labs. There won't be further updates to these pages. ]

Refinement Using Maximal Graphs

(Related publication: [PUBLink])

Total-order, partial-order and hierarchical planners create and reason about the plan's structures step by step. In contrast, planners like Graphplan [PUBLink] create a large maximal structure that includes all potential plans before starting the search process. Superfluous/inconsistent elements are then removed by the search process. The search can exploit these structures much better because propagation can involve the reasoning on parts of the plan for which no decisions have been made. However, maximal structures do not scale well. Other examples of this approach are parcPLAN [PUBLink] and CPlan [PUBLink].


[REFINEMENT SEARCH]   [Total-Order Planning]   [Partial-Order Planning]   [Hierarchical Planning]   [Maximal Graphs]

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Last update:
May 19, 2001 by Alexander Nareyek