Summary of A Coalition Game For On-demand Multi-modal 3d Automated Delivery System, by Farzan Moosavi and Bilal Farooq
A Coalition Game for On-demand Multi-modal 3D Automated Delivery System
by Farzan Moosavi, Bilal Farooq
First submitted to arxiv on: 23 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a multi-modal autonomous delivery optimization framework for last-mile delivery in urban environments. The framework uses coalition game theory to model cooperation among UAVs and ADRs operating in two overlaying networks. The problem is defined as multiple depot pickup and delivery with time windows constrained by operational restrictions such as vehicle battery limitation, precedence time window, and building obstruction. The authors design a generalized reinforcement learning model to evaluate cost-sharing and allocation to different coalitions, leveraging an end-to-end deep multi-agent policy gradient method with a novel spatio-temporal adjacency neighbourhood graph attention network and transformer architecture using a heterogeneous edge-enhanced attention model. Numerical experiments on last-mile delivery applications demonstrate the effectiveness of the framework in addressing realistic operational constraints and achieving high-quality solutions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way for drones and trucks to work together to deliver packages quickly and efficiently in cities. It’s like a game where they decide how to share tasks to get everything delivered on time. The problem is that there are many obstacles, like building heights and truck routes, that need to be considered. The authors use special computer algorithms to solve this complex problem. They tested their method with real-world data from the city of Mississauga and found that it worked well even when things didn’t go exactly as planned. |
Keywords
» Artificial intelligence » Attention » Graph attention network » Multi modal » Optimization » Reinforcement learning » Transformer
