Logical Stochastic Optimization
This work addresses stochastic optimization problems for researchers in AI and operations research, but it appears incremental as it builds upon existing probability answer set programming methods.
The authors tackled the problem of representing and reasoning about stochastic optimization problems by introducing a logical framework based on probability answer set programming, which allows for minimization or maximization under probabilistic environments, and demonstrated its application to two-stage stochastic optimization problems with recourse.
We present a logical framework to represent and reason about stochastic optimization problems based on probability answer set programming. This is established by allowing probability optimization aggregates, e.g., minimum and maximum in the language of probability answer set programming to allow minimization or maximization of some desired criteria under the probabilistic environments. We show the application of the proposed logical stochastic optimization framework under the probability answer set programming to two stages stochastic optimization problems with recourse.