  We consider a stochastic variant of the packing-type integer linear
programming problem, which contains random variables in the objective vector.
We are allowed to reveal each entry of the objective vector by conducting a
query, and the task is to find a good solution by conducting a small number of
queries. We propose a general framework of adaptive and non-adaptive algorithms
for this problem, and provide a unified methodology for analyzing the
performance of those algorithms. We also demonstrate our framework by applying
it to a variety of stochastic combinatorial optimization problems such as
matching, matroid, and stable set problems.
