Learning and Reasoning in Hybrid Structured Spaces.

• Superior document: Frontiers in Artificial Intelligence and Applications Series ; v.350
• Author: Morettin, P.
• 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
• Physical Description: 1 online resource (112 pages)

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.