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Clément GIRAUD/Edwin MOREL
Customizable Game of Life with Predator-Prey Dynamics
Customizable Game of Life with Predator-Prey Dynamics is a variant of Conway's Game of Life that allows players to customize the rules of cell creation and introduces predator-prey interactions between wolves and sheep. The game is played on a grid of cells, where each cell can be in one of two possible states: alive or dead. Additionally, wolves and sheep are introduced as agents that interact with each other and the grid.
Features:
Grid Representation:
The game is played on a two-dimensional grid of cells.
Each cell can be in one of two states: alive or dead.
The grid wraps around at the edges, creating a toroidal topology.
Customizable Creation Rules:
Players can customize the rules for cell creation, determining under what conditions a dead cell becomes alive.
This feature allows for experimentation with different rulesets, leading to diverse patterns and behaviors.
Predator-Prey Dynamics:
Wolves and sheep are introduced as agents that interact with each other and the grid.
Wolves hunt and eat sheep, while sheep attempt to evade wolves to survive.
This introduces additional complexity and emergent behavior to the simulation.
Simulation Controls:
Users can start, pause, resume, and stop the simulation.
The initial state of the grid can be customized by the user or generated randomly.
Gameplay:
Upon starting the simulation, the initial state of the grid, along with wolves and sheep, is displayed.
The simulation evolves according to the rules of Conway's Game of Life, as well as the interactions between wolves and sheep.
Players can observe the dynamic patterns and structures that emerge as the simulation progresses.
The simulation continues until the user decides to pause or stop it.
Purpose:
Customizable Game of Life with Predator-Prey Dynamics provides an interactive platform for exploring emergent behavior and complexity in cellular automata. By customizing creation rules and introducing predator-prey dynamics, players can observe how different factors influence pattern formation and ecosystem dynamics.
Future Enhancements:
Further customization options for predator-prey interactions, such as predator hunting strategies and prey evasion behaviors.
Integration of additional agent types and interactions to simulate more complex ecosystems.
Implementation of statistical analysis tools to study population dynamics and pattern evolution.