YODA Lab

The Yeoh’s Optimization and Decision Analytics (YODA) Lab at Washington University in St. Louis uses artificial intelligence based techniques to develop intelligent agent-based systems. Historically, the group has used methods based on decision theory, constraint programming, and heuristic search for intelligent single- and multi-agent systems.

More recently, the group has focused on interdisciplinary approaches to enable human-AI collaboration through human-aware decision-making algorithms. Please visit our research page to find more information on our current research projects!

recent news

Jul 2024: KR paper on argumentation-based dialectical explanations accepted.
Jun 2024: CP papers on incomplete DCOPs and communication-aware DCOPs accepted.
Apr 2024: IJCAI paper on multi-objective search accepted.
Dec 2023: AAMAS paper on polarization in social networks accepted.
Aug 2023: Received a BSF grant with Roie Zivan to work on explainable DCOPs!

selected publications

  1. AIJ
    Simple and Efficient Bi-Objective Search Algorithms via Fast Dominance Checks
    Carlos Hernández, William Yeoh, Jorge A. Baier, and 4 more authors
    Artificial Intelligence, 2023
  2. JAIR
    Communication-Aware Local Search for Distributed Constraint Optimization
    Ben Rachmut, Roie Zivan, and William Yeoh
    Journal of Artificial Intelligence Research, 2022
  3. JAIR
    A Logic-Based Explanation Generation Framework for Classical and Hybrid Planning Problems
    Stylianos Loukas Vasileiou, William Yeoh, Tran Cao Son, and 3 more authors
    Journal of Artificial Intelligence Research, 2022
  4. AAMAS
    Algorithmic Filtering, Out-Group Stereotype, and Polarization on Social Media
    Jean Springsteen, William Yeoh, and Dino Christenson
    In International Conference on Autonomous Agents and Multiagent Systems, 2024
  5. ICAPS
    Using Simple Incentives to Improve Two-Sided Fairness in Ridesharing Systems
    Ashwin Kumar, Yevgeniy Vorobeychik, and William Yeoh
    In International Conference on Automated Planning and Scheduling, 2023