[PlanetKR] CFP: AAAI-17 Workshop on Symbolic Inference and Optimization

Vaishak Belle vaishak at cs.toronto.edu
Thu Oct 6 20:35:38 EST 2016


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


San Francisco, CA, USA, 4/5 February 2016

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

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

Encouraged by these successes, the workshop aspires to bring together AI
researchers from knowledge representation, machine learning, databases,
verification and planning to bettٰer understand applications of symbolic
methods to inference and optimization problems across all fields.

Workshop topics:

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 bettٰer 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.

Important Dates:
 * Papers submission: November 4, 2016 (contact organizers regarding late
 * Notifications of acceptance: November 18, 2016 (for on-time submissions)
 * Camera-ready to AAAI: December 8, 2016
 * Workshop date:  February 4 or 5, 2016 (one day)

Submission Procedure:

We welcome previously unsubmitted work, papers submitted to the main AAAI
conference, and papers reporting research already published provided they
align well with the workshop topic.

Three types of submissions are solicited:

- full-length papers (up to 6 pages + 1 page for references in AAAI format)
- challenge or position papers (2 pages + 1 page for references in AAAI
- already published papers (1 page: an abstract in AAAI format with a link
to the full paper)

Paper Submissions should be made through the workshop EasyChair web site


 * Scott Sanner, University of Toronto (contact: ssanner[at]mie.utoronto.ca)
 * Vaishak Belle, University of Edinburgh (contact:
 * Rodrigo de Salvo Braz, SRI International (contact: braz[at]ai.sri.com)
 * Kristian Kersting, TU Dortmund (contact:

More information about the PlanetKR mailing list