  In this paper, we propose a novel approach for solving linear numeric
planning problems, called Symbolic Pattern Planning. Given a planning problem
$\Pi$, a bound $n$ and a pattern -- defined as an arbitrary sequence of actions
-- we encode the problem of finding a plan for $\Pi$ with bound $n$ as a
formula with fewer variables and/or clauses than the state-of-the-art rolled-up
and relaxed-relaxed-$\exists$ encodings. More importantly, we prove that for
any given bound, it is never the case that the latter two encodings allow
finding a valid plan while ours does not. On the experimental side, we consider
6 other planning systems -- including the ones which participated in this
year's International Planning Competition (IPC) -- and we show that our planner
Patty has remarkably good comparative performances on this year's IPC problems.
