Representations, Inference and Learning for Complex Decision Making Under Uncertainty

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This proposal is about decision making under uncertainty in domains where there are rich relational descriptions, such as making treatment decisions about medical patients conditioned on the detailed observations in their electronic health records, or making a decision about prospecting in a geographic region conditioned on a description of the local geology. Building on recent progress in statistical relational AI, probabilistic programming, ontological reasoning and preference elicitation, the aim of this proposal is to design a coherent expressive framework for reasoning and decision making under uncertainty.******The mix of probabilistic models with relational models has become known as statistical relational AI. These models specify both the logical structure (in terms of quantified logical variables) and the probabilistic structure (in terms of conditional independence). The problem of exploiting both, called lifted inference has recently been essentially solved for the exact undirected relational case by a number of research teams. Part of this proposal is to build on the recent successors to build the next generation of relational probabilistic systems with expressive representations, efficient inference and robust learning. We will also further advance exact inference, which will also enable new ways to do efficient approximate inference.******Recently there has been an explosion of scientific and government data being published with the vocabulary defined by formal ontologies. We will work on representing, reasoning and learning hypotheses that interoperate with heterogeneous data sets and rich ontologies. There are exciting challenges that arise when defining multiple hypotheses at various levels of abstraction and detail that make predictions on multiple heterogeneous data sets.******Ultimately these models are used to make decisions. The other part of making decisions is utilities. We will also work on preference elicitation for utility models that interact with the relational models and ontologies, and are understandable by decision makers.******Our work will build on ongoing collaboration with real-world decision makers in geology, computational sustainability, and medicine. This proposal is to develop the computational foundations to make such applications feasible.***********