| Full text | |
| Author(s): |
Maua, Denis Deratani
;
Cozman, Fabio Gagliardi
Total Authors: 2
|
| Document type: | Journal article |
| Source: | NEURIPS WORKSHOPS, 2020; v. 215, p. 12-pg., 2023-01-01. |
| Abstract | |
Probabilistic Answer Set Programming offers an intuitive and powerful declarative language to represent uncertainty about combinatorial structures. Remarkably, under the credal semantics, such programs can specify any infinitely monotone Choquet Capacity in an intuitive way. Yet, one might be interested in specifying more general credal sets. We examine how probabilistic answer set programs can be extended to represent more general credal sets with constructs that allow for imprecise probability values. We characterize the credal sets that can be captured with various languages, and discuss the expressivity and complexity added by the use of imprecision in probabilistic constructs. (AU) | |
| FAPESP's process: | 19/07665-4 - Center for Artificial Intelligence |
| Grantee: | Fabio Gagliardi Cozman |
| Support Opportunities: | Research Grants - Research Program in eScience and Data Science - Research Centers in Engineering Program |
| FAPESP's process: | 22/02937-9 - Neural inductive logic programming |
| Grantee: | Denis Deratani Mauá |
| Support Opportunities: | Research Grants - Initial Project |