AAAI-22 Workshop on AI for Decision Optimization

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.

Program

February 28, 2022. All times are in PST (Pacific Coast US).

 

06:45 - 07:40 AM	Introduction and Keynote
	AI for Engineering Pascal Van Hentenryck, the A. Russell Chandler III Chair and Professor, and the associate chair of innovation and entrepreneurship in the H. Milton Steward School of Industrial and Systems Engineering at the Georgia Institute of Technology Abstract: This talk argues that the fusion of machine learning and optimization is a promising avenue to address fundamental challenges in engineering domains, including in power systems, logistics and supply chains, mobility, and circuit design. The presentation presents various case studies where optimization proxies, learning to optimize, and end-to-end learning are pushing the frontiers in these domains. Bio Pascal Van Hentenryck is the A. Russell Chandler III Chair and Professor in the H. Milton Stewart School of Industrial and Systems Engineering at the Georgia Institute of Technology and the Associate Chair for Innovation and Entrepreneurship. He is the director of the NSF AI Institute for Advances in Optimization, the director of the Socially Aware Mobility (SAM) and the Risk-Aware Market Clearing (RAMC) labs. Several of his optimization systems have been in commercial use for more than 20 years for solving logistics, supply-chains, and manufacturing applications. His current research focuses on machine learning, optimization, and privacy with applications in energy, mobility, and supply chains.
07:40 - 07:45 AM	Break
07:45 - 08:30 AM	Lightning Demo Session
	07:45 AM Knowledge-driven Decision Optimization for Non-Experts Yishai A. Feldman, Aviad Sela and Segev Wasserkrug 08:00 AM OptiCL: A Package for Mixed-Integer Optimization with Constraint Learning Donato Maragno, Holly Wiberg, Dimitris Bertsimas, S. Ilker Birbil, Dick den Hertog and Adejuyigbe Fajemisin 08:15 AM Automated Decision Optimization: Data and Knowledge-driven Optimization Model Generation with Human-in-the-loop Lisa Amini, Arunima Chaudhary, Yishai Feldman, Pavankumar Murali, Lam Nguyen, Dzung Phan, Aviad Sela, Carolina Spina, Dharmashankar Subramanian, Abel Valente, Long Vu, Dakuo Wang, Segev Wasserkrug, Ritesh Yadav and Nianjun Zhou
08:30 - 08:45 AM	Break
08:45 - 10:00 AM	Paper Session
	08:45 AM Almost Hyperparameter-free Hyperparameter Selection Framework for Offline Policy Evaluation Kohei Miyaguchi 09:00 AM A Prediction-based Approach for Online Dynamic Radiotherapy Scheduling Tu-San Pham, Antoine Legrain and Louis-Martin Rousseau 09:15 AM GDI: Rethinking What Makes Reinforcement Learning Different from Supervised Learning Jiajun Fan, Changnan Xiao and Yue Huang 09:30 AM Logical Neural Networks to Serve Decision Making with Meaning Lan Hoang and Alexander Zadorojniy 09:45 AM Automated Decision Optimization with Reinforcement Learning Radu Marinescu, Tejaswini Pedapati, Long Vu, Paulito Palmes, Todd Mummert, Peter Kirchner, Dharmashankar Subramanian, Parikshit Ram and Djallel Bouneffouf
10:00 - 10:30 AM	Long Break
10:30 - 11:00 AM	Special Talk
	Optimization with Constraint Learning: A Framework and Survey Extended Abstract Adejuyigbe Fajemisin, Donato Maragno and Dick den Hertog
11:00 AM - 12:00 PM	Panel Driven Brainstorming Session
	The Future of AI for Decision Optimization Panel Members: Prof. David Bergman, University of Connecticut Prof. Bistra Dilkina, USC Prof. Pascal Van Hentenryck, Georgia Institute of Technology Prof. Frank Hutter, Freiburg University Prof. Michele Lombardi, University of Bologna Dr. Segev Wasserkrug, IBM Research Holy Wiberg, MIT
12:00 - 12:30 PM	Break and Breakout Discussions / Networking
12:30 - 01:45 PM	Paper Session
	12:30 PM Predict and Optimize: Through the Lens of Learning to Rank Jayanta Mandi, Victor Bucarey Lopez, Maxime Mulamba Ke Tchomba and Tias Guns 12:45 PM Addressing Solution Quality in Data-generated Optimization Models Orit Davidovich, Parikshit Ram, Segev Wasserkrug, Dharmashankar Subramanian, Nianjun Zhou, Dzung Phan, Pavankumar Murali and Lam Nguyen 01:00 PM Constraint Acquisition and the Data Collection Bottleneck Steve Prestwich 01:15 PM End-to-End Learning via Constraint-Enforcing Approximators for Linear Programs with Applications to Supply Chains Rares Cristian, Pavithra Harsha, Georgia Perakis, Brian Quanz and Ioannis Spantidakis 01:30 PM JUMBO: Scalable Multi-task Bayesian Optimization Using Offline Data Kourosh Hakhamaneshi, Pieter Abbeel, Vladimir Stojanovic and Aditya Grover
01:45 - 02:00 PM	Summary and Next Steps Discussion
02:00 PM	End of Workshop

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 eight pages in length (including references).
• Short paper: Submission of work in progress with preliminary results, and position papers, up to four pages in length (+ references).
• Extended abstract: Summary of recently published journal/conference papers in the form of a two-pages extended abstracts.
• Fast Track (Rejected AAAI papers): AAAI papers that were rejected in the second round of reviews, 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' comments.

Software tool submissions should include:
• A two-page short paper (+ one-page reference) describing the tool, which should include the technical details of the tool and related work, and describe the tool's significance and relevance to the workshop topics.
• A demonstration video (or publicly available link of such video) of 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.

Organization

Workshop chairs:

• Prof. Bistra Dilkina, USC
• Dr. Segev Wasserkrug, IBM Research

Workshop organizing committee:

• Prof. Andrea Lodi, Jacobs Technion-Cornell Institute - IIT
• Dr. Dharmashankar Subrmanian, IBM Research

Workshop program committee:

• Sina Aghaei, University of Southern California (USC)
• Weizhe Chen, University of Southern California (USC)
• Didier Chételat, Polytechnique Montréal
• Orit Davidovich, IBM Research
• Maxime Gasse, Polytechnique Montréal
• Lan Hoang, IBM Research
• Haoming Li, University of Southern California (USC)
• Defeng Liu, Polytechnique Montréal
• Takayuki Osogami, IBM Research
• Horst Samulowitz, IBM Research
• Evgeny Shindin, IBM Research
• Alexander Zadorojniy, IBM Research