Parameterized Complexity Results for Exact Bayesian Network Structure Learning
| Authors | |
|---|---|
| Year of publication | 2013 |
| Type | Article in Periodical |
| Magazine / Source | JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH |
| MU Faculty or unit | |
| Citation | |
| Doi | http://dx.doi.org/10.1613/jair.3744 |
| Field | Information theory |
| Keywords | probabilistic network structure learning; parameterized complexity;algorithms |
| Description | The propositional planning problem is a notoriously difficult computational problem, which remains hard even under strong syntactical and structural restrictions. Given its difficulty it becomes natural to study planning in the context of parameterized complexity. In this paper we continue the work initiated by Downey, Fellows and Stege on the parameterized complexity of planning with respect to the parameter ``length of the solution plan.'' We provide a complete classification of the parameterized complexity of the planning problem under two of the most prominent syntactical restrictions, i.e., the so called PUBS restrictions introduced by B{\"a}ckstr\"{o}m and Nebel and restrictions on the number of preconditions and effects as introduced by Bylander. We also determine which of the considered fixed-parameter tractable problems admit a polynomial kernel and which don't. |
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