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Please use this identifier to cite or link to this item:
http://hdl.handle.net/1903/517
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| Title: | SHOP and M-SHOP: Planning with Ordered Task Decomposition |
| Authors: | Nau, Dana Cao, Yue Lotem, Amnon Munoz-Avila, Hector |
| Type: | Technical Report |
| Issue Date: | 17-Jun-2000 |
| Series/Report no.: | UM Computer Science Department; CS-TR-4157 |
| Abstract: | SHOP (Simple Hierarchical Ordered Planner) and M-SHOP (Multi-task-list
SHOP) are planning algorithms with the following characteristics.
* SHOP and M-SHOP plan for tasks in the same order that they will later
be executed. This avoids some task-interaction issues that arise in
other HTN planners, making the planning algorithms relatively
simple. This also makes it easy to prove soundness and completeness
results.
* Since SHOP and M-SHOP know the complete world-state at each step of the
planning process, they can use highly expressive domain
representations. For example, they can do planning problems that
require Horn-clause inferencing, complex numeric computations, and
calls to external programs.
* In our tests, SHOP and M-SHOP were several orders of magnitude faster
than Blackbox, IPP, and UMCP, and were several times as fast as
TLplan.
* The approach is powerful enough to be used in complex real-world
planning problems. For example, we are using a Java implementation of
SHOP as part of the HICAP plan-authoring system for Noncombatant
Evacuation Operations (NEOs).
In this paper, we describe SHOP and M-SHOP, present soundness and
completeness results for them, and compare them experimentally to
Blackbox, IPP, TLplan, and UMCP. The results suggest that planners that
generate totally ordered plans starting from the initial state can "scale
up" to complex planning problems better than planners that use partially
ordered plans. |
| URI: | http://hdl.handle.net/1903/517 |
| Appears in Collections: | Technical Reports of the Computer Science Department
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