  This paper studies the possibilities made open by the use of Lazy Clause
Generation (LCG) based approaches to Constraint Programming (CP) for tackling
sequential classical planning. We propose a novel CP model based on seminal
ideas on so-called lifted causal encodings for planning as satisfiability, that
does not require grounding, as choosing groundings for functions and action
schemas becomes an integral part of the problem of designing valid plans. This
encoding does not require encoding frame axioms, and does not explicitly
represent states as decision variables for every plan step. We also present a
propagator procedure that illustrates the possibilities of LCG to widen the
kind of inference methods considered to be feasible in planning as (iterated)
CSP solving. We test encodings and propagators over classic IPC and recently
proposed benchmarks for lifted planning, and report that for planning problem
instances requiring fewer plan steps our methods compare very well with the
state-of-the-art in optimal sequential planning.
