  In this paper, a possibilistic disjunctive logic programming approach for
modeling uncertain, incomplete and inconsistent information is defined. This
approach introduces the use of possibilistic disjunctive clauses which are able
to capture incomplete information and incomplete states of a knowledge base at
the same time.
  By considering a possibilistic logic program as a possibilistic logic theory,
a construction of a possibilistic logic programming semantic based on answer
sets and the proof theory of possibilistic logic is defined. It shows that this
possibilistic semantics for disjunctive logic programs can be characterized by
a fixed-point operator. It is also shown that the suggested possibilistic
semantics can be computed by a resolution algorithm and the consideration of
optimal refutations from a possibilistic logic theory.
  In order to manage inconsistent possibilistic logic programs, a preference
criterion between inconsistent possibilistic models is defined; in addition,
the approach of cuts for restoring consistency of an inconsistent possibilistic
knowledge base is adopted. The approach is illustrated in a medical scenario.
