---
title: "Apprenticeship Scheduling: Learning to Schedule from Human Experts"
authors:
  - "Matthew C. Gombolay"
  - "Reed Jensen"
  - "Jessica Stigile"
  - "Sung-Hyun Son"
  - "Julie A. Shah"
doi: ""
url: "https://ijcai.org/proceedings/2016/122"
pdf: "https://ijcai.org/proceedings/2016/0122.pdf"
venue: "Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence"
year: 2016
pages: "826-832"
track: ""
keywords:
---

# Apprenticeship Scheduling: Learning to Schedule from Human Experts

Matthew C. Gombolay, Reed Jensen, Jessica Stigile, Sung-Hyun Son, Julie A. Shah

*Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence*, pages 826-832.

## Abstract

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the single-expert, single-trainee apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes, causing the codification of this knowledge to become laborious. We propose a new approach for capturing domain-expert heuristics through a pairwise ranking formulation. Our approach is model-free and does not require enumerating or iterating through a large state-space. We empirically demonstrate that this approach accurately learns multifaceted heuristics on both a synthetic data set incorporating job-shop scheduling and vehicle routing problems and a real-world data set consisting of demonstrations of experts solving a weapon-to-target assignment problem.