Efficient algorithm for estimation of qualitative expected utility in
  possibilistic case-based reasoning
  We propose an efficient algorithm for estimation of possibility based
qualitative expected utility. It is useful for decision making mechanisms where
each possible decision is assigned a multi-attribute possibility distribution.
The computational complexity of ordinary methods calculating the expected
utility based on discretization is growing exponentially with the number of
attributes, and may become infeasible with a high number of these attributes.
We present series of theorems and lemmas proving the correctness of our
algorithm that exibits a linear computational complexity. Our algorithm has
been applied in the context of selecting the most prospective partners in
multi-party multi-attribute negotiation, and can also be used in making
decisions about potential offers during the negotiation as other similar
problems.
