In this paper, we present a procedure for numeric planning based on Symbolic Pattern Planning (SPP). Given a numeric planning problem
Π, a pattern
≺ is a sequence of actions used to define a formula encoding the subsequences of
≺ executable from a starting state
S. Cardellini, Giunchiglia, and Maratea (2024a) follow the Planning as Satisfiability approach by defining, at each step
n≥0, a formula
Πn≺ in which
(i) the pattern
≺ is computed only for
n=0 in the initial state
I of
Π, and then exploited at each step
n,
(ii) the starting state
S is set to
I, and
(iii) the set
G of goals is required to hold in the last state that can be reached by one of the subsequences of
≺ concatenated
n times. The procedure begins with
n=0, terminates as soon as
Πn≺ is satisfiable, and otherwise proceeds by incrementing
n. In this paper, possibly at each step,
(i) we symbolically search for an intermediate state
P reachable from
I, closer to a goal state,
(ii) dynamically recompute the pattern
≺h -- to be used in the next step -- in
P,
(iii) refine the pattern
≺g used to reach
P, and
(iv) start the new search from the state
S which can be either the initial state
I or the last computed intermediate state
P, exploiting the computed patterns
≺g and
≺h to define the pattern
≺ to be used in the search. In particular, at each step, we define a formula
ΠS,P≺ encoding the existence of a state
P′ closer than
P to a goal state, with
P′ reachable from the starting state
S when using the pattern
≺. We present different techniques for producing such formulas, each corresponding to a different strategy for exploring the search space. We prove their correctness and completeness, the latter under certain conditions.