Learning and Reasoning in Hybrid Structured Spaces.
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| Superior document: | Frontiers in Artificial Intelligence and Applications Series ; v.350 |
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| Place / Publishing House: | Amsterdam : : IOS Press, Incorporated,, 2022. Ã2022. |
| Year of Publication: | 2022 |
| Edition: | 1st ed. |
| Language: | English |
| Series: | Frontiers in Artificial Intelligence and Applications Series
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| Online Access: | |
| Physical Description: | 1 online resource (112 pages) |
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Table of Contents:
- Intro
- Title Page
- Abstract
- Acknowledgments
- Contents
- Introduction
- Motivation
- Contributions
- Outline of the Thesis
- Background
- Probabilistic Graphical Models
- Bayesian Networks
- Markov Networks
- Factor graphs
- The belief propagation algorithm
- Inference by Weighted Model Counting
- Propositional satisfiability
- Weighted Model Counting
- Logical structure
- Inference by Weighted Model Integration
- Satisfiability Modulo Theories
- Weighted Model Integration
- Related work
- Modelling and inference
- Learning
- WMI-PA
- Predicate Abstraction
- Weighted Model Integration, Revisited
- Basic case: WMI Without Atomic Propositions
- General Case: WMI With Atomic Propositions
- Conditional Weight Functions
- From WMI to WMIold and vice versa
- A Case Study
- Modelling a journey with a fixed path
- Modelling a journey under a conditional plan
- Efficiency of the encodings
- Efficient WMI Computation
- The Procedure WMI-AllSMT
- The Procedure WMI-PA
- WMI-PA vs. WMI-AllSMT
- Experiments
- Synthetic Setting
- Strategic Road Network with Fixed Path
- Strategic Road Network with Conditional Plans
- Discussion
- Final remarks
- MP-MI
- Preliminaries
- Computing MI
- Hybrid inference via MI
- On the inherent hardness of MI
- MP-MI: exact MI inference via message passing
- Propagation scheme
- Amortizing Queries
- Complexity of MP-MI
- Experiments
- Final remarks
- lariat
- Learning WMI distributions
- Learning the support
- Learning the weight function
- Normalization
- Experiments
- Final remarks
- Conclusion.