Journal of Artificial Intelligence Research (JAIR), Volume 75, Pages 489-539, October 2022
A novel approach to epistemic planning called planning with perspectives (PWP) that is both more expressive and computationally more efficient than existing state-of-the-art epistemic planning tools. The method decomposes epistemic planning by delegating reasoning about epistemic formulae to an external solver using Functional STRIPS.
Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS), Volume 33, Number 1, Pages 180-188, July 2023
Extends the PWP model to handle multi-agent belief by following the intuition that agents believe something they have seen until they see otherwise. Introduces the notion of justified perspectives and demonstrates that the belief planner can solve benchmarks more efficiently than state-of-the-art baselines.
Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (CHI EA '25), 2025
Explores the feasibility of modeling higher-order human beliefs using the Justified Perspective (JP) model. Proposes a novel processing algorithm inspired by Item Response Theory to estimate reasoning abilities based on participants' responses to diverse reasoning scenarios. Demonstrates the promise of integrating epistemic planning frameworks with human-centered applications.
PhD Research Thesis, University of Melbourne, 2025
Supervisors: Timothy Miller and Nir Lipovetzky
Extends the Justified Perspective model to develop a more efficient and effective framework for epistemic planning. Introduces multiple semantic formats to clarify the balance between efficiency and completeness, and extends the model to handle justified beliefs and group beliefs (distributed and common beliefs). Demonstrates that the approach is both more efficient and expressive than the current state-of-the-art in epistemic planning.
Master of Philosophy Research Thesis, University of Melbourne, 2020
Supervisors: Timothy Miller and Nir Lipovetzky
Decomposes epistemic planning by delegating epistemic logic reasoning to an external solver using functional STRIPS. Demonstrates how to model epistemic planning more expressively and efficiently by allowing modellers to provide implementations of external functions that define what agents see in their environment. Results show that the approach scales significantly better than state-of-the-art planners and can express problems more succinctly.
CoRR, arXiv:2412.07941, 2024
Extends the Justified Perspective model with predictive capabilities for epistemic planning beyond static assumptions.
CoRR, arXiv:2412.07981, 2024
Extends the Justified Perspective model to represent group beliefs, including distributed beliefs and common beliefs. Addresses the limitations of common knowledge and demonstrates how common belief can be formed and represented in multi-agent planning scenarios.