2013/04/06 by Emad Saad, Saad, Emad
Computer Science · #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Logic, Reasoning, and Knowledge #Multi-Agent Systems and Negotiation #cs.AI
paper · pdf · doi:10.48550/arxiv.1304.3489
arXiv admin note: substantial text overlap with arXiv:1304.2384, arXiv:1304.2797, arXiv:1304.1684, arXiv:1304.3144
arxiv created 2013/04/06 · openalex publication_date 2013/04/06 · arxiv updated 2013/04/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.