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.

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

Azzolini D.
Primo
2023

Abstract

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.
2023
978-3-031-49298-3
978-3-031-49299-0
Constrained Optimization
Parameter Learning
Probabilistic Answer Set Programming
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/2532951
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