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About the Workshop
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 the 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-based optimization.
- 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, and 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 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.
Important Dates
Extended submission date: | November 16, 2021 |
AAAI Fast Track submission deadlines: | December 5, 2021 |
Notification of acceptance: | December 13, 2021 |
AAAI-22 Workshop Program: | February 28, 2022 |
Registration in each workshop is required by all active participants, and is also open to all interested individuals. Early registration deadline is on December 31. For more information please refer to https://aaai.org/Conferences/AAAI-22/registration/.
Related workshop
You are also invited to join the AAAI Workshop on Machine Learning for Operations Research (ML4OR-22) that will take place on March 1.