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Probabilistic Answer Set Programming with Discrete and Continuous Random Variables

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Publicado en:Theory and Practice of Logic Programming vol. 25, no. 1 (Jan 2025), p. 1
Autor principal: Azzolini, Damiano
Otros Autores: Riguzzi, Fabrizio
Publicado:
Cambridge University Press
Materias:
Semantics
Random variables
Continuity (mathematics)
Boolean
Mathematical programming
Logic programming
Probability theory
Algorithms
Probability
Declarative programming
Enumeration
Sampling
Real variables
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Resumen:Probabilistic Answer Set Programming under the credal semantics extends Answer Set Programming with probabilistic facts that represent uncertain information. The probabilistic facts are discrete with Bernoulli distributions. However, several real-world scenarios require a combination of both discrete and continuous random variables. In this paper, we extend the PASP framework to support continuous random variables and propose Hybrid Probabilistic Answer Set Programming. Moreover, we discuss, implement, and assess the performance of two exact algorithms based on projected answer set enumeration and knowledge compilation and two approximate algorithms based on sampling. Empirical results, also in line with known theoretical results, show that exact inference is feasible only for small instances, but knowledge compilation has a huge positive impact on performance. Sampling allows handling larger instances but sometimes requires an increasing amount of memory.
ISSN:1471-0684
1475-3081
DOI:10.1017/S1471068424000437
Fuente:Advanced Technologies & Aerospace Database