Summary of Optimal Task Assignment and Path Planning Using Conflict-based Search with Precedence and Temporal Constraints, by Yu Quan Chong et al.
Optimal Task Assignment and Path Planning using Conflict-Based Search with Precedence and Temporal Constraints
by Yu Quan Chong, Jiaoyang Li, Katia Sycara
First submitted to arxiv on: 13 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Multiagent Systems (cs.MA)
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| 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 presents a new framework for the Multi-Agent Path Finding (MAPF) problem, which involves guiding agents from their start to goal locations while avoiding collisions. The existing MAPF formulation does not consider practical constraints such as task-related actions with specific execution times, predetermined orders and timeframes, or user-defined objectives. To address these limitations, the paper introduces the Task Assignment and Path Finding with Precedence and Temporal Constraints (TAPF-PTC) problem. A novel algorithm, CBS-TA-PTC, is developed by augmenting Conflict-Based Search (CBS) to simultaneously generate task assignments and collision-free paths that adhere to precedence and temporal constraints. The algorithm maximizes a user-defined reward function in reinforcement learning (RL). Experimental results show that CBS-TA-PTC efficiently solves highly challenging bomb-defusing tasks with precedence and temporal constraints, outperforming state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tackles a complex problem called Multi-Agent Path Finding, where agents need to move from start to goal locations without crashing. Currently, this problem doesn’t consider important details like taking actions at specific times or following rules. To make it more realistic, the authors introduce a new challenge that includes these constraints. They create an algorithm that can solve this new problem by assigning tasks and finding paths for the agents. The algorithm is tested on a difficult example of defusing bombs while following rules, and it performs well. |
Keywords
* Artificial intelligence * Reinforcement learning
