  Hierarchical Task Network (HTN) planning uses task decomposition to plan for
an executable sequence of actions as a solution to a problem. In order to
reason effectively, an HTN planner needs expressive domain knowledge. For
instance, a simplified HTN planning system such as JSHOP2 uses such
expressivity and avoids some task interactions due to the increased complexity
of the planning process. We address the possibility of simplifying the domain
representation needed for an HTN planner to find good solutions, especially in
real-world domains describing home and building automation environments. We
extend the JSHOP2 planner to reason about task interaction that happens when
task's effects are already achieved by other tasks. The planner then prunes
some of the redundant searches that can occur due to the planning process's
interleaving nature. We evaluate the original and our improved planner on two
benchmark domains. We show that our planner behaves better by using simplified
domain knowledge and outperforms JSHOP2 in a number of relevant cases.
