# Interpretable and Personalized Apprenticeship Scheduling: Learning Interpretable Scheduling Policies from Heterogeneous User Demonstrations

arXiv: https://arxiv.org/abs/1906.06397 · HTML: https://mlsanity.com/paper/1906.06397/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2019.

## Built on by (most cited first, 14 of 14)

- [From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI](https://mlsanity.com/paper/2201.08164/) (2022)
- [Benchmarks and Algorithms for Offline Preference-Based Reward Learning](https://mlsanity.com/paper/2301.01392/) (2023)
- [The Utility of Explainable AI in Ad Hoc Human-Machine Teaming](https://mlsanity.com/paper/2209.03943/) (2022)
- [Offline Preference-Based Apprenticeship Learning](https://mlsanity.com/paper/2107.09251/) (2021)
- [Fast Lifelong Adaptive Inverse Reinforcement Learning from Demonstrations](https://mlsanity.com/paper/2209.11908/) (2022)
- [Conditional Imitation Learning for Multi-Agent Games](https://mlsanity.com/paper/2201.01448/) (2022)
- [Learning Interpretable, High-Performing Policies for Autonomous Driving](https://mlsanity.com/paper/2202.02352/) (2022)
- [Learning Models of Adversarial Agent Behavior under Partial Observability](https://mlsanity.com/paper/2306.11168/) (2023)
- [FedEmbed: Personalized Private Federated Learning](https://mlsanity.com/paper/2202.09472/) (2022)
- [Towards Balancing Preference and Performance through Adaptive Personalized Explainability](https://mlsanity.com/paper/2504.13856/) (2025)
- [Improving Robot-Centric Learning from Demonstration via Personalized Embeddings](https://mlsanity.com/paper/2110.03134/) (2021)
- [Towards Sample-efficient Apprenticeship Learning from Suboptimal Demonstration](https://mlsanity.com/paper/2110.04347/) (2021)
- [Strategy Discovery and Mixture in Lifelong Learning from Heterogeneous Demonstration](https://mlsanity.com/paper/2202.07014/) (2022)
- [FedPC: Federated Learning for Language Generation with Personal and Context Preference Embeddings](https://mlsanity.com/paper/2210.03766/) (2022)

## Abstract (arXiv)

Resource scheduling and coordination is an NP-hard optimization requiring an efficient allocation of agents to a set of tasks with upper- and lower bound temporal and resource constraints. Due to the large-scale and dynamic nature of resource coordination in hospitals and factories, human domain experts manually plan and adjust schedules on the fly. To perform this job, domain experts leverage heterogeneous strategies and rules-of-thumb honed over years of apprenticeship. What is critically needed is the ability to extract this domain knowledge in a heterogeneous and interpretable apprenticeship learning framework to scale beyond the power of a single human expert, a necessity in safety-critical domains. We propose a personalized and interpretable apprenticeship scheduling algorithm that infers an interpretable representation of all human task demonstrators by extracting decision-making criteria via an inferred, personalized embedding non-parametric in the number of demonstrator types. We achieve near-perfect LfD accuracy in synthetic domains and 88.22% accuracy on a planning domain with real-world, outperforming baselines. Finally, our user study showed our methodology produces more interpretable and easier-to-use models than neural networks (p < 0.05).