Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/juan-alvarado21/plants-vs-zombies-multi-agent-system-using-a-algorithm
This program based on plants vs zombies simulates the problem of the sample collecting robot (multi-agent system) proposed to be carried out with the A* informed search algorithm where the samples will be found and grouped into clusters, using crumbs to trace the path to the ship. and describe the collaboration between agents.
https://github.com/juan-alvarado21/plants-vs-zombies-multi-agent-system-using-a-algorithm
a-star-algorithm heuristic-search-algorithms informed-search multiagent-systems reactive-agents
Last synced: about 2 months ago
JSON representation
This program based on plants vs zombies simulates the problem of the sample collecting robot (multi-agent system) proposed to be carried out with the A* informed search algorithm where the samples will be found and grouped into clusters, using crumbs to trace the path to the ship. and describe the collaboration between agents.
- Host: GitHub
- URL: https://github.com/juan-alvarado21/plants-vs-zombies-multi-agent-system-using-a-algorithm
- Owner: Juan-Alvarado21
- License: mit
- Created: 2024-05-10T09:12:25.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-05-10T10:01:41.000Z (8 months ago)
- Last Synced: 2024-05-11T10:29:33.576Z (8 months ago)
- Topics: a-star-algorithm, heuristic-search-algorithms, informed-search, multiagent-systems, reactive-agents
- Language: JavaScript
- Homepage:
- Size: 8.2 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Plants-vs-zombies-multi-agent-system-using-A-algorithm
This program, based on Plants vs. Zombies, simulates the problem of the sample-collecting robot (multi-agent system) proposed to be addressed using the A* informed search algorithm. In this system, samples are found and grouped into clusters, using crumbs to trace the path back to the ship, and the collaboration between agents is described.
