AAAI-17 Workshop on Symbolic Inference and Optimization

Workshop co-organised by Vaishak Belle

The purpose of the workshop is to explore and promote symbolic approaches to probabilistic inference, numerical optimization and machine learning. The workshop will place a special emphasis on techniques for mixed discrete/continuous (hybrid) domains and techniques that can be extended to such domains.

Symbolic approaches enjoy a long and distinguished history in AI. While the last two decades have seen major advances in probabilistic modeling, data management, data fusion and datadriven learning, much of this work assumes fairly lowlevel representations that are tailored for a specific application. It is now recognized that formal languages, and their symbolic underpinnings, can enable descriptive clarity, reusability, and interpretability, thereby furthering the applicability and impact of AI technology.

For more information on how to submit papers, see