Aint Yo Mamas Golding Novel

Aint Yo Mamas Golding Novel

Follow these steps to play the game

This Aint Yo Mamas William Golding Novel is an online multiplayer turn-based strategy game designed for the Nonoba platform. The goal is simple: survive until you can get rescued. You’ll need to decide whether to wait for the coast guard to come rescue you, or whether to build a boat to try to rescue yourself. You’ll need to have enough food and water to eat and drink each night, otherwise you’ll start taking damage. You’ll also want to find or build shelter, or you’ll take damage from rain.

It’s up to you whether you want to work together with your fellow players, or eat them for food. There are several viable strategies! For more information, read the in-game tips and mouse-overs.

To setup a game, go to the website and then click “Create Game” after entering the game lobby. Pick a name for your game instance, select the number of players, then press “Create.” Other players will now see the game appear in the lobby (though they may need to refresh your browser). Once all players have joined, the game will begin.

The game can be played single-player, but please note that the game should be tried with four or more players in order to get the full intended experience.

Description

This Aint Yo Mamas William Golding Novel is an online multiplayer turn-based strategy game inspired by William Golding’s Lord of the Flies and the boardgame Arkham Horror. The goal is to survive on a tropical island with limited resources. Players can either wait for the coast guard to come rescue them, or build a boat to try to sail away. The core feature is that any number of players can win or lose; each player “wins” by surviving, rather than by “beating” the other players. It’s up to the players themselves whether they want to work together, or eat each other for food!

The game represents the first phase of a Masters thesis project at IT University of Copenhagen. The project focuses on procedurally generating maps via advanced machine learning algorithms. By classifying gameplay data from previous playthroughs, the project aims to dynamically determine a set of player types that can be used to open up new game design possibilities. For instance, the game might generate maps that play towards the players’ identified types, or perhaps even maps that play against the players’ types, in the hopes of encouraging them to play differently.

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