SymInfOpt 2017 - 2017 Workshop on Symbolic Inference and Optimization (SymInfOpt-17)

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 data‐driven learning, much of this work assumes fairly low‐level representations that are tailored for a specific application. It is now recognized that formal languages, and their symbolic underpinnings, can enable descriptive clarity, re‐usability, and interpretability, thereby furthering the applicability and impact of AI technology.
Recently, there have been significant successes of formal representations and symbolic techniques for inference and optimization. In the area of probabilistic modeling, weighted model counting has emerged as a competitive and general paradigm, providing state‐of‐the‐art inference for graphical models, Markov Logic Networks, and probabilistic programming. In the area of planning, symbolic approaches have been shown to handle large state spaces by leveraging abstractions. In the area of verification, real‐time systems in robotics and even pacemakers have been successfully checked for safety and correctness using symbolic specifications. In the area of optimization and learning, symbolic approaches ranging from symbolic algebra to SMT to decision diagrams have enabled novel scalable solutions.
Encouraged by these successes, the workshop aspires to bring together AI researchers from knowledge representation, machine learning, databases, verification and planning to bett?er understand applications of symbolic methods to inference and optimization problems across all fields.
Call for Submissions
Topics include (but are not limited to)
symbolic methods for inference (e.g., SMT)
symbolic approaches for handling both discrete and continuous probability spaces
symbolic planning and scheduling approaches
symbolic approaches to bett?er represent and solve optimization problems
symbolic and algebraic methods in machine learning
Because purely logical inference is well-covered at AI, we would like to de-emphasize such submissions unless they cover probabilistic, mixed discrete/continuous, arithmetic, optimization, or other novel uses / expressive extensions of logical inference. Please contact the organizers if unsure about your potential submission's relevance for this workshop.