
## Right Place, Right Time: Proactive Multi-Robot Task Allocation Under Spatiotemporal Uncertainty
Charlie Street, Bruno Lacerda, Manuel Mühlig, Nick Hawes
Keywords: 
JAIR/2024/Proceedings/15057 - Right Place, Right Time: Proactive Multi-Robot Task Allocation Under Spatiotemporal Uncertainty.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?_

[10]

Implementation is not given. The authors state the method is written in Python and in part in Java/C++. No other details.

### 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?_

[1]

(1/1)

The authors use simulated environments in 6.1.1. and describe them with a visualisation and cite the implementation. 

### 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?_

[10]

No parameters discussed.

### 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 measure total waiting duration in seconds (explained) over increasing team size. In table 1 they present the same data but as mean waiting durations. Each experiment is repeated 40 times for the population. 

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

[8]

Requires expertise on multi-agent allocation and markov chains.
