A Constraint-based Encoding for Domain-Independent Temporal Planning
  We present a general constraint-based encoding for domain-independent task
planning. Task planning is characterized by causal relationships expressed as
conditions and effects of optional actions. Possible actions are typically
represented by templates, where each template can be instantiated into a number
of primitive actions. While most previous work for domain-independent task
planning has focused on primitive actions in a state-oriented view, our
encoding uses a fully lifted representation at the level of action templates.
It follows a time-oriented view in the spirit of previous work in
constraint-based scheduling. As a result, the proposed encoding is simple and
compact as it grows with the number of actions in a solution plan rather than
the number of possible primitive actions. When solved with an SMT solver, we
show that the proposed encoding is slightly more efficient than
state-of-the-art methods on temporally constrained planning benchmarks while
clearly outperforming other fully constraint-based approaches.
