The AAAI-22 workshop on AI for Decision Optimization (AI4DO) will explore how AI can be used to significantly simplify the creation of efficient production level optimization models, thereby enabling their much wider application and resulting business
value. The desired outcome of this workshop is to drive forward research and seed collaborations in this area by bringing together machine learning and decision-making from the lens of both dynamic and static optimization models.
* Aim and Topics
The goals of the workshop are to create joint awareness of the current state-of-the-art, including relevant software tools, and seed collaborations towards radical simplification of the creation and deployment of decision optimization models. This will
be carried out through open discussions, presentations of relevant works and tools, and exchange of ideas by researchers and practitioners working at the relevant intersection of Data Science, AI and Operations Research, in areas such as machine learning,
symbolic AI, mathematical programming, simulation and heuristic based optimization, constraint optimization and learning, sequential decision making and reinforcement learning, dynamic control, planning with Markov Models (MDPs, POMDPs), and learning
Topics of interest include, but are not limited to:
- Learning decision optimization models from data, including learning mathematical optimization formulations (objectives and constraints), learning constraints from data, and creating reinforcement learning models from data.
- Learning dynamic models from data, including state transition models, reward models, as well as dynamic models for uncontrollable environmental uncertainties with a view toward automatically constructing environments against which agents may learn
without being limited by interaction with and/or sample complexity of real systems.
- Techniques for automated search for optimal decision-making pipelines, such as next-generation AutoML and AutoRL: frameworks that can combine structured tabular data and expressive model-related knowledge to automatically synthesize and evaluate
decision-making pipelines that are made up of any combination of automated data transformation, automated data-driven and knowledge-driven system models, and automated model-based and data-based learning of optimal decisions.
- Techniques for accommodating model inaccuracies in data-driven learning of empirical system models, along with techniques that restrict decisions to regions of acceptable data-sufficiency thereby limiting unsafe extrapolation when learning empirical
system models from data.
- Techniques for high level specification of the knowledge required to create such models by users such as domain experts, data scientists and developers, and the derivation of such decision optimization models from such knowledge.
- Relevant decision-focused work, such as works extending decision-focused learning to dynamic problems, and combining decision-focused machine learning with classical machine learning to handle objective functions and system constraints that are
respectively learned empirically from historical data.
- Ensuring the end-to-end accuracy and performance of data and knowledge based generated models, by accounting for the limitations of the data and knowledge used to generate them.
- Techniques for speeding up the solution time of such generated models, including specification of knowledge that can be used to speed up the solution time, and using AI to accelerate the solution of such automatically generated models (including
combinatorial optimization problems), to ensure that these models can be solved in an acceptable amount of time.
Submissions due: November 12, 2021
AAAI Fast Track submission deadlines: TBA
Notification of acceptance: December 3, 2021
AAAI-22 Workshop Program: February 28 or
March 1, 2022 (TBD)
## Call for workshop papers ##
Authors are invited to send contributions of both relevant works and relevant software tools, both in the AAAI-22 proceedings format (see AAAI author kit). Submission is through the AAAI-22 AI4DO Workshop Easychair Submission Link.
Contributions of relevant work should be in one of the following forms:
- Long paper: Submission of original work up to 8 pages in length (including references).
- Short paper: Submission of work in progress with preliminary results, and position papers, up to 4 pages in length (+ references).
- Extended abstract: Summary of recently published journal/conference papers in the form of a 2-pages extended abstracts.
- Fast Track (Rejected NeurIPS or AAAI papers): Rejected NeurIPS or AAAI papers with *average* scores of at least 5.0 may be submitted along with previous reviews and scores and an optional letter indicating how the authors have addressed the reviewers
Software tools submissions should include:
- A 2-page short paper (+1 page reference) describing the tool, which should include the technical details of the tool, related work, and describe the tool's significance and relevance to the workshop topics.
- A demonstration video (or publicly available link of such video) to the tool, or link to a relevant website (which could also be a github link) from where the tool can be downloaded.
The review process is single-blind. The program committee will select the papers and tools to be presented at the workshop according to their suitability to the aims.