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UBC Theses and Dissertations

Generalized constraint-based inference Chang, Le

Abstract

Constraint-Based Inference (CBI) is a unified framework that subsumes many practical problems in different research communities. These problems include probabilistic inference, decision-making under uncertainty, constraint satisfaction, propositional satisfiability, decoding problems, and possibility inference. Solving them efficiently is important for both research and practical applications. Along with the development of inference approaches for concrete CBI problems, researchers are increasingly aware that these problems share common features in representation and essentially identical inference approaches. As the first contribution of this thesis, we explicitly use the semiring concept to generalize various CBI problems into a single formal representation framework with a broader coverage of the problem space based on the synthesis of existing generalized frameworks. Second, the proposed semiring-based unified framework is also a single formal algorithmic framework that provides a broader coverage of both exact and approximate inference algorithms, including variable elimination, junction tree, and loopy message propagation methods. Third, we discuss inference algorithm design and complexity issues based on the abstract representations of CBI problems and inference algorithms. These discussions are examples of applying the abstract knowledge to the concrete application domains. Researchers from various fields can (1) study the most important common characteristics of various CBI problems without representation barriers; (2) analyze and compare different inference approaches; (3) borrow design ideas from other fields and improve the inference approaches' efficiency in their own domains; and (4) significantly reduce the amount of implementation work target ted previously at the individual problems. Finally, we present a software toolkit named the Generalized Constraint- Based Inference Toolkit in Java (GCBIJ) as the last contribution of this thesis. GCBIJ is the first concrete software toolkit that implements the abstract semiring approach to unify the CBI problem representations and the inference algorithms. Users can design their own task-specific semirings or simply use ones provided by the toolkit to solve their own concrete CBI problems through instantiating various already provided abstract inference algorithms. Users can also design their own inference algorithms on the abstract level and apply them to solve different problems. Furthermore, researchers can test, verify, compare, and analyze inference approaches based on this toolkit. The the experimental results based on GCBIJ show that the generalized CBI framework is a useful tool for both research and practical problem-solving.

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