Many real world AI applications involve reasoning on both
continuous and discrete variables, while requiring some level of
symbolic reasoning that can provide guarantees on the system's
behaviour. Unfortunately, most of the existing probabilistic models do
not efficiently support hard constraints or they are limited to purely
discrete or continuous scenarios.
Weighted Model Integration (WMI) is a recent and general formalism
that enables probabilistic modeling and inference in hybrid structured
domains. A difference of WMI-based inference algorithms with respect
to most alternatives is that probabilities are computed inside
a structured support involving both logical and algebraic
relationships between variables.
While some progress has been made in the last years and the topic is
increasingly gaining interest from the community, research in this
area is at an early stage.
These aspects motivate the study of hybrid and symbolic probabilistic
models and the development of scalable inference procedures and
effective learning algorithms in these domains.
This PhD Thesis embodies my effort in studying scalable reasoning and
learning techniques in the context of WMI.
| Identifer | oai:union.ndltd.org:unitn.it/oai:iris.unitn.it:11572/264203 |
| Date | 29 May 2020 |
| Creators | Morettin, Paolo |
| Contributors | Morettin, Paolo, Passerini, Andrea, Sebastiani, Roberto |
| Publisher | Università degli studi di Trento, place:Trento |
| Source Sets | Università di Trento |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/doctoralThesis |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | firstpage:1, lastpage:100, numberofpages:100 |
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