Ant Colony Sim

AntColonySim is a basic top-down simulation game in which an ant nest is positioned near the center of the map. Food, water, and poison are randomly distributed throughout the environment. The ants roam the map, searching for resources. Once an ant finds food, it returns to the nest to drop it off, satisfying its hunger and spawning a new ant. Afterward, the ant becomes thirsty and must locate a water source before gathering more food. The presence of poison adds a hazard: if an ant encounters poison, it dies immediately.

This game uses a combination of a wandering algorithm and A* Pathfinding. When the 'ants' are exploring the map they move randomly until they encounter, either water, poison or food. Water has no change on movement patterns, poison kills and removes the ant from the simulation, but when it encounters food it will want to return to the nest. The ant will use A* Pathfinding to find its way back to the nest in the most efficent way possible.

A* Pathfinding

The game is a basic top down game, You can make a map for an AI "Ant" to find its way through to the goal position or use one of the premade maps. Once initiated, the game shows the tiles it looks at and the final path. The display of showing the path finding is not in real time. After the ant makes it home the game will display the time it took.

The game makes use of A*Pathfinding and using a heuristic approach of selecting the closest tile to the goal tile. A simple game meant to visualize how pathfinding works. The game also takes into account terrain difficulty, each different type of ground has a different cost to travel trhough, this makes it so that even the direct approach with no obstacles may be the wrong approach.

Connect 4

Everyone has heard of the classic connect 4 game. You play against an AI opponent and whoever puts 4 chips in the row is the victor.

Connect 4 utilizes a Minimax algorithm, enhanced with alpha-beta pruning, to determine the optimal move. The current implementation is designed to evaluate potential outcomes up to four moves ahead, using alpha-beta pruning to efficiently reduce the number of branches explored and improve decision-making speed.

Ice Escape

The ice is fracturing beneath your feet, leaving the bot with no option but to adapt and survive for as long as possible by skillfully avoiding the crumbling ice.

This game employs reinforcement learning, where the bot is rewarded based on its actions. For instance, the bot earns the highest rewards for remaining on solid ice, receives penalties for falling into the water, and is assigned varying points for navigating cracked ice. There is also a chance of forced randomness where the bot makes a random decision to discover new possibilities, after each round, the randomness chance decreases.

Valley Escape

The game is a simple top-down adventure where the objective is to escape the area while avoiding roaming skeletons. To succeed, the player must collect a key to unlock the gate and make their escape. However, picking up the key triggers all the skeletons to become aware of the player's location, causing them to hunt the player as a unified group. After escaping, the player advances to the next level, which features the same objective

The games goal is display different forms of enemy AI movement. In this case we have a wandering movement, where they just walk around. We have a seeking movement, where when the player gets close enough, the skeletons begin to chase. And we have an item that will scare away skeletons for a short period, so a flee movement. There is alsoa group coordiantion movement that occurs when the player grabs the key. Where the skeletons move together in a triangle formation to the player.