Name
Abstract | ||
|---|---|---|
Star-topology decoupling is a state space search method recently introduced in AI Planning. It decomposes the input model into components whose interaction structure has a star shape. The decoupled search algorithm enumerates transition paths only for the center component, maintaining the leaf-component state space separately for each leaf. This is a form of partial-order reduction, avoiding interleavings across leaf components. It can, and often does, have exponential advantages over stubborn set pruning and unfolding. AI Planning relates closely to model checking of safety properties, so the question arises whether decoupled search can be successful in model checking as well. We introduce a first implementation of star-topology decoupling in SPIN, where the center maintains global variables while the leaves maintain local ones. Preliminary results on several case studies attest to the potential of the approach. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-319-94111-0_6 | Lecture Notes in Computer Science |
DocType | Volume | ISSN |
Conference | 10869 | 0302-9743 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel Gnad | 1 | 12 | 6.89 |
| Patrick Dubbert | 2 | 0 | 0.34 |
| Alberto Lluch-Lafuente | 3 | 706 | 51.82 |
| Jörg Hoffmann | 4 | 2702 | 189.88 |