---
title: "Co-Optimizating Multi-Agent Placement with Task Assignment and Scheduling"
authors:
  - "Chongjie Zhang"
  - "Julie A. Shah"
doi: ""
url: "https://ijcai.org/proceedings/2016/468"
pdf: "https://ijcai.org/proceedings/2016/0468.pdf"
venue: "Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence"
year: 2016
pages: "3308-3313"
track: ""
keywords:
---

# Co-Optimizating Multi-Agent Placement with Task Assignment and Scheduling

Chongjie Zhang, Julie A. Shah

*Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence*, pages 3308-3313.

## Abstract

To enable large-scale multi-agent coordination under temporal and spatial constraints, we formulate it as a multi-level optimization problem and develop a multi-abstraction search approach for co-optimizing agent placement with task assignment and scheduling. This approach begins with a highly abstract agent placement problem and the rapid computation of an initial solution, which is then improved upon using a hill climbing algorithm for a less abstract problem; finally, the solution is fine-tuned within the original problem space. Empirical results demonstrate that this multi-abstraction approach significantly outperforms a conventional hill climbing algorithm and an approximate mixed-integer linear programming approach.