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Project
Learning and decision theory in relational dynamic domains.
Statistical relational models combine aspects of first-order logic and probabilistic graphical models, enabling them to model complex logical and probabilistic interactions between large numbers of objects. This level of expressivity comes at the cost of increased complexity of inference, motivating a new line of research in lifted probabilistic inference. By exploiting symmetries of the relational structure in the model, and reasoning about groups of objects as a whole, lifted algorithms dramatically improve the run time of inference and learning.
The thesis has five main contributions. First, we propose a new method for logical inference, calledfirst-order knowledge compilation. We show that by compiling relational models into a new circuit language, hard inference problems become tractable to solve. Furthermore, we present an algorithm that compiles relational models into our circuit language. Second, we show how to use first-order knowledge compilation for statistical relational models, leading to a new state-of-the-art lifted probabilistic inference algorithm. Third, we develop a formal framework for exact lifted inference, including a definition in terms of its complexity w.r.t. the number of objects in the world. From this follows a first completeness result, showing that the two-variable class of statistical relational models always supports lifted inference. Fourth, we present an algorithm for approximate lifted inference by performing exact lifted inference in a relaxed, approximate model. Statistical relational models are receiving a lot of attention today because of their expressive power for learning. Fifth,we propose to harness the full power of relational representations for that task, by using lifted parameter learning. The techniques presented in this thesis are evaluated empirically on statistical relational models of thousands of interacting objects and millions of random variables.
The thesis has five main contributions. First, we propose a new method for logical inference, calledfirst-order knowledge compilation. We show that by compiling relational models into a new circuit language, hard inference problems become tractable to solve. Furthermore, we present an algorithm that compiles relational models into our circuit language. Second, we show how to use first-order knowledge compilation for statistical relational models, leading to a new state-of-the-art lifted probabilistic inference algorithm. Third, we develop a formal framework for exact lifted inference, including a definition in terms of its complexity w.r.t. the number of objects in the world. From this follows a first completeness result, showing that the two-variable class of statistical relational models always supports lifted inference. Fourth, we present an algorithm for approximate lifted inference by performing exact lifted inference in a relaxed, approximate model. Statistical relational models are receiving a lot of attention today because of their expressive power for learning. Fifth,we propose to harness the full power of relational representations for that task, by using lifted parameter learning. The techniques presented in this thesis are evaluated empirically on statistical relational models of thousands of interacting objects and millions of random variables.
Date:1 Oct 2009 → 30 Sep 2013
Keywords:Decision, Learning, Dynamic domains
Disciplines:Artificial intelligence, Cognitive science and intelligent systems, Applied mathematics in specific fields, Computer architecture and networks, Distributed computing, Information sciences, Information systems, Programming languages, Scientific computing, Theoretical computer science, Visual computing, Other information and computing sciences
Project type:PhD project