Solving Decision Theory Problems with Probabilistic Answer Set Programming
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912236264488960 |
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| author | Azzolini, Damiano Bellodi, Elena Kiesel, Rafael Riguzzi, Fabrizio |
| author_facet | Azzolini, Damiano Bellodi, Elena Kiesel, Rafael Riguzzi, Fabrizio |
| contents | Solving a decision theory problem usually involves finding the actions, among a set of possible ones, which optimize the expected reward, 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. Under consideration in Theory and Practice of Logic Programming (TPLP). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_11371 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Solving Decision Theory Problems with Probabilistic Answer Set Programming Azzolini, Damiano Bellodi, Elena Kiesel, Rafael Riguzzi, Fabrizio Artificial Intelligence Logic in Computer Science Solving a decision theory problem usually involves finding the actions, among a set of possible ones, which optimize the expected reward, 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. Under consideration in Theory and Practice of Logic Programming (TPLP). |
| title | Solving Decision Theory Problems with Probabilistic Answer Set Programming |
| topic | Artificial Intelligence Logic in Computer Science |
| url | https://arxiv.org/abs/2408.11371 |