  Soft goals extend the classical model of planning with a simple model of
preferences. The best plans are then not the ones with least cost but the ones
with maximum utility, where the utility of a plan is the sum of the utilities
of the soft goals achieved minus the plan cost. Finding plans with high utility
appears to involve two linked problems: choosing a subset of soft goals to
achieve and finding a low-cost plan to achieve them. New search algorithms and
heuristics have been developed for planning with soft goals, and a new track
has been introduced in the International Planning Competition (IPC) to test
their performance. In this note, we show however that these extensions are not
needed: soft goals do not increase the expressive power of the basic model of
planning with action costs, as they can easily be compiled away. We apply this
compilation to the problems of the net-benefit track of the most recent IPC,
and show that optimal and satisficing cost-based planners do better on the
compiled problems than optimal and satisficing net-benefit planners on the
original problems with explicit soft goals. Furthermore, we show that
penalties, or negative preferences expressing conditions to avoid, can also be
compiled away using a similar idea.
