Solving Decision Theory Problems with Probabilistic Answer Set Programming
This work addresses decision-making under uncertainty for AI and logic programming researchers, but appears incremental as it builds on existing methods with specific algorithmic improvements.
The paper tackles the problem of solving decision theory problems by encoding them with Probabilistic Answer Set Programming under credal semantics, and proposes an algorithm based on Algebraic Model Counting that manages non-trivial instances in reasonable time.
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).