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Solving Decision Theory Problems with Probabilistic Answer Set Programming

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Publicado en:Theory and Practice of Logic Programming vol. 25, no. 1 (Jan 2025), p. 33
Autor principal: Azzolini, Damiano
Otros Autores: BELLODI, ELENA, Kiesel, Rafael, Riguzzi, Fabrizio
Publicado:
Cambridge University Press
Materias:
Decision theory
Semantics
Mathematical programming
Expected utility
Aggregates
Viral marketing
Logic programming
Variables
Algorithms
Declarative programming
Enumeration
Synthetic data
Acceso en línea:Citation/Abstract
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Resumen:Solving a decision theory problem usually involves finding the actions, among a set of possible ones, which optimize the expected reward, while possibly accounting for the uncertainty of the environment. In this paper, we introduce the possibility to encode decision theory problems with Probabilistic Answer Set Programming under the credal semantics via decision atoms and utility attributes. To solve the task, we propose an algorithm based on three layers of Algebraic Model Counting, that we test on several synthetic datasets against an algorithm that adopts answer set enumeration. Empirical results show that our algorithm can manage non-trivial instances of programs in a reasonable amount of time.
ISSN:1471-0684
1475-3081
DOI:10.1017/S1471068424000474
Fuente:Advanced Technologies & Aerospace Database