explainable planning and scheduling

In human-aware planning and scheduling systems, when the agent recommends a plan or schedule to a human user, it is often the case that the user might not understand why the recommendation is good, for example, compared to an alternative in the user’s mind. In such a scenario, there is a need for the agent to explain its recommendation to the user, providing them with the necessary information to understand properties of the recommendation (e.g., optimality, feasibility, etc.).

We are approaching this problem from a knowledge representation and reasoning (KR) perspective, where we represent the mental models of both the agent and the human user using logical facts and rules. Within this framework, we adapt and generalize KR notions (e.g., entailment, hitting sets, model counting) to solve this problem.

To learn more, please see our invited talk on this topic, which we gave at University of Alberta. Slides available here.


Collaborative Research: RI: Small: End-to-end Learning of Fair and Explainable Schedules for Court Systems.
National Science Foundation (2023 – 2026).

Explainable Distributed Constraint Optimization.
Binational Science Foundation (2023 – 2026).

Explainable and Privacy-Aware Decentralized Scheduling.
J.P. Morgan Chase Bank (2023 – 2024).

Improving Client Experience Through Goal Recognition and Explainable Assistance in Adaptive Systems.
J.P. Morgan Chase Bank (2022 – 2023).

RI: Small: Collaborative Research: Preference Elicitation and Device Scheduling for Smart Homes.
National Science Foundation (2018 – 2021).

selected publications

  1. ECAI
    A Logic-Based Framework for Explainable Agent Scheduling Problems
    Stylianos Loukas Vasileiou, Borong Xu, and William Yeoh
    In European Conference on Artificial Intelligence, 2023
  2. ECAI
    PLEASE: Generating Personalized Explanations in Human-Aware Planning
    Stylianos Loukas Vasileiou, and William Yeoh
    In European Conference on Artificial Intelligence, 2023
  1. 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
  2. ICAPS
    VizXP: A Visualization Framework for Conveying Explanations to Users in Model Reconciliation Problems
    Ashwin Kumar, Stylianos Loukas Vasileiou, Melanie Bancilhon, and 2 more authors
    In International Conference on Automated Planning and Scheduling, 2022
  1. AAAI
    On Exploiting Hitting Sets for Model Reconciliation
    Stylianos Loukas Vasileiou, Alessandro Previti, and William Yeoh
    In AAAI Conference on Artificial Intelligence, 2021
  2. JELIA
    Model Reconciliation in Logic Programs
    Tran Cao Son, Van Nguyen, Stylianos Loukas Vasileiou, and 1 more author
    In European Conference on Logics in Artificial Intelligence, 2021