Automatic parameter selection for multi-speedmulti-agent pathfinding solver
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Typ
Examensarbete för masterexamen
Program
Computer systems and networks (MPCSN), MSc
Publicerad
2020
Författare
ROSENFELD, ANDREAS
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Multi-agent pathfinding is a study focused on finding collision-free paths for multiple
agents in a shared environment in order for the agents to reach designated destinations.
As the problem is well researched, more and more sophisticated algorithms
are available that utilize different parameters to fine-tune the search for these solutions.
In this paper, we demonstrate the combining of multi-agent pathfinding with
machine learning to perform such parameter selection dynamically. This selection
is approached as an image classification problem, where unseen problem instances
are divided between a collection of available parameter values. For this, typical vehicular
road traffic scenarios were implemented for experimentation. The presented
results give new knowledge for this previously unstudied approach indicating that
there are cases in which visual representation of the problem is sufficient as the
dynamic selection can perform better when compared to a fixed setting. Therefore,
demonstrating the potential that machine learning can be used to improve the
performance of a multi-agent pathfinding algorithm without needing to develop it
further.
Beskrivning
Ämne/nyckelord
Computer science , multi-agent pathfinding , machine learning , transfer learning , AlexNet , parameter selection , urban scenarios