  Many AI synthesis problems such as planning or scheduling may be modelized as
constraint satisfaction problems (CSP). A CSP is typically defined as the
problem of finding any consistent labeling for a fixed set of variables
satisfying all given constraints between these variables. However, for many
real tasks such as job-shop scheduling, time-table scheduling, design?, all
these constraints have not the same significance and have not to be necessarily
satisfied. A first distinction can be made between hard constraints, which
every solution should satisfy and soft constraints, whose satisfaction has not
to be certain. In this paper, we formalize the notion of possibilistic
constraint satisfaction problems that allows the modeling of uncertainly
satisfied constraints. We use a possibility distribution over labelings to
represent respective possibilities of each labeling. Necessity-valued
constraints allow a simple expression of the respective certainty degrees of
each constraint. The main advantage of our approach is its integration in the
CSP technical framework. Most classical techniques, such as Backtracking (BT),
arcconsistency enforcing (AC) or Forward Checking have been extended to handle
possibilistics CSP and are effectively implemented. The utility of our approach
is demonstrated on a simple design problem.
