  We establish a novel relation between delete-free planning, an important task
for the AI Planning community also known as relaxed planning, and logic
programming. We show that given a planning problem, all subsets of actions that
could be ordered to produce relaxed plans for the problem can be bijectively
captured with stable models of a logic program describing the corresponding
relaxed planning problem. We also consider the supported model semantics of
logic programs, and introduce one causal and one diagnostic encoding of the
relaxed planning problem as logic programs, both capturing relaxed plans with
their supported models. Our experimental results show that these new encodings
can provide major performance gain when computing optimal relaxed plans, with
our diagnostic encoding outperforming state-of-the-art approaches to relaxed
planning regardless of the given time limit when measured on a wide collection
of STRIPS planning benchmarks.

Artificial Intelligence