
## Expected 1.x Makespan-Optimal Multi-Agent Path Finding on Grid Graphs in Low Polynomial Time
Teng Guo, Jingjin Yu
Keywords: 
JAIR/2024/Proceedings/16299 - Expected 1.x Makespan-Optimal Multi-Agent Path Finding on Grid Graphs in Low Polynomial Time.pdf
Project URL: 

### Implementation
_Given the documentation given by the authors on the method, how much time investment would it be to re-implement the method from scratch?_

[9]

The authors state in section 7 they implement the methods in C++ and some other methods (with link/citation) used for parts of their code. Implementation not given.

### Data
_Given the data description in the documentation, how much effort take to either: Find the same dataset the authors used, or similar datasets and defend the comparability, or acquire one from scratch?_

[6]

(1/1)

The authors randomly generate instances and describe the process in 7.1. Implementation not given.

### Configuration 
_Given the (hyper)parameters, including semantic parameters, of the method: How much effort would it take to acquire the algorithm configurations used for their results, and compare against their budgetary constraints?_

[1]

Based on the pseudo code and the experiments section, it seems the methods are parameter free.

### Experimental Procedure
_Given the experimental set-up of the work, how difficult is it to set up a new experiment, similar to those presented in the original work, with the same procedure?_

[1]

The authors present the results as an average over 20 runs on randomly generated instances with a running time limit over 300 seconds. The authors measure optimality ratio, optimality gap (defined in section 3/4) and computation time. 

### Expertise
_How much effort would it take to acquire the expertise required to reproduce the work independently relying on the available documentation?_

[7]

Requries expertise on multi-agent path finding.
