A Constrained Optimization Approach to Set the Parameters of Probabilistic Answer Set Programs

Probabilistic Answer Set Programming under the credal semantics has emerged as one of the possible formalisms to encode uncertain domains described by an answer set program extended with probabilistic facts. Some problems require associating probability values to probabilistic facts such that the probability of a query is above a certain threshold. To solve this, we propose a new class of programs, called Probabilistic Optimizable Answer Set Programs, together with a practical algorithm based on constrained optimization to solve the task.