AI in Space Group 3 Raquel Cohen Yesenia De La Cruz John Gratton

What is AI In Space? AI In Space is a collection of 3 different types of Artificial Intelligence each demonstrated in a unique mini game. Each mini game is going to be a level of the overall game and in each level the user will be able to attack, race, or shoot at very unique enemy AI's. Each group member was in charge of designing a level just for the AI that they have selected to implement.

The AI’s in the Game Level 1 – Neat Level 2 – Neural Networks and Genetic Algorithm. Level 3 – Neat Real Time 7/31/12

Story Line Our heroic alien, Lerioc, has been out cast from his planet and must now find his way back. He needs to journey through three different environments: Red Arena – Level 1 Mazy Way – Level 2 Home – Level 3

Tools: Blender

Tools: SpaceScape

Tools: Unity3D

Level One A 1 on 1 tournament style mini game that has you face off with the best enemy on this unknown planet. This enemy has been training its whole life just to fight, so watch out as this enemy can move fast and fire even faster. Placed in an empty arena figure out a way to quickly take out the enemy before it takes you out. Use plasma fire to shoot down your enemy and collect health packs around the arena to give you a second chance. Be careful it only takes 1 shot to take you down but with the help of a health pack you can live a little longer.

Level One How To To move the red player on the screen use the arrow keys: the left and right arrows are used to rotate and the up arrow is used to move forward. Use the space-bar on the keyboard to fire your plasma. Note you can only fire plasma after it has been fully recharged. Pick up health packs that are around the arena to get a boost of health. Stay out of the plasma glow around the enemy to avoid getting hit.

Level One AI The artificial intelligence that is used for level 1 is: co- evolution, using NEAT, and the dominance tournament. Co-evolution trains two neural networks at the same time. Each network will be able to learn from one another. NEAT makes the networks more advance and allows the neural networks to become more complex. Dominance tournament allows us to track which neural network is the best out of all the networks.

Co-evolution with NEAT Co-evolution allows individual fitness based on the number of rounds won by a neural network. Example: If neural network wins a round it will get a plus 1 if it loses or ties then no score will be added. NEAT allows the neural networks to grow and still keep track of past innovations.

NEAT Class Diagram

Neural Network

Dominance Tournament Tracks the best neural network out of all trained networks. Allows for a guarantee of the best network. Example: if d3 beats d2 and d2 beats d1 we can say d3 is the best, but what happens if d1 beats d3. This is what the dominance prevents. Dominance tournament states that in order to be the best you have to beat all of the other best.

Level One Training Data The time it took on average to complete one generation was 1 hour. The time it took to complete one generation with a lot winners was about minutes. The time to complete 10 generations > 10 hours After each generation the dominance tournament was ran and took about 1-3 minutes.

Level One Troubles Time to train slowed everything down. Have the neural networks been advanced enough. Getting real 3d graphics. Showing that the artificial intelligence is an actual AI and not something like FSM.

Level 2 – Maze Race Race between the Player and the AI character. The one to get to the goal first wins.

Maze Maps Prototype used for training. Final Design of Maze

Dual Camera View Player Character AI Character

Environment The background is black with stars to simulate space. The walls of the maze were created using Blender, and they look like asteroids. Blender was also used to create the spaceship of the player and of the enemy.

Player Character Space Shuttle looking. Controlled by the arrow keys or by the ‘A’ ‘S’ ‘W’ ‘D’ keys. The object is going to rotate with the left and right keys of the ‘A’ and ‘D’ keys. The object is going to move forward and backwards with the up and down arrows and with the ‘W’ and ‘S’ keys.

AI Character The enemy looks like a UFO. Controlled by the artificial intelligence algorithm. The enemy will have sensors that are going to give the inputs to the AI. The AI is then going to output the displacement of the enemy.

Artificial Intelligence Neural Networks with a Genetic Algorithm 2 outputs 5 inputs

Neural Network: Inputs Inputs are going to come from the sensors. Sensors are rays that come out of the character and feel the world around it. These rays are going to be invisible to the player.

Neural Network: Inside the Neuron Inside the neuron: Activation value = Neuron:

Neural Network: Outputs The sigmoid curve and the activation value are used to calculate the output of the neuron.

How The Genetic Algorithm Works First a population of 40 individuals (Neural Networks) is created with random weight values. Each individual is left to run and a fitness value is assigned depending on its performance. 7/31/12

How The Genetic Algorithm Works The genetic algorithm then looks at this population and its fitness values to create a new one with better weights on each individual. The new generation then runs to get fitness values and again enter the GA for the next generation. After many generations, all the individuals should be at around the same fitness value. 7/31/12

Training Population = 40 individuals Individuals were given 1 minute each to traverse the maze. The GA ran for 21 generations That is a total of 14 hours of training!

Enemy at Generation 1 7/31/12

Enemy at Generation 12 7/31/12

Level 3- Game Play Dodge Ball Concept Offers a better chances of a successful Real Time learning. Player controls a single player Enemies are continuously regenerating. Player wins if hits a certain number of enemies within a time limit. Player loses if each member if he/she is killed before the time limit or goal is not met before the time limit. Players and enemies can shoot at the same time.

Game Objects ObjectFunctionNumber User-ModelModel the user will have control of in order to play the game. 1 EnemyModels that will be controlled by the neural networks.18 ShotsObjects that will collide with either user or enemy StadiumWhere the contest will occur. For appearance as well as preventing user or enemy from going beyond boundaries. 1 Point CounterDisplays the number of enemies the user has eliminated verses the total amount needed to win the game. 1 TimerCounts up to limit. Lets user know how much time she/he has left until game is over. 1 Light SourceFor appearance purposes.5 TerrainAppearance1 Health CounterLet’s the user know how much health is left1 Intro to LevelFor entertainment purposes and example directions.1

Overall Game Classes Minion In charge of non-player characters AgentController In charge of player’s characters Collision Main In charge of overall follow of game. Runs NEAT, checks timer, calculates number of ticks, initialize brains MinionShots In charge of the shots made by minion Shots In charge of the player’s shots

Neural Networks

Minions- Visual

Real Time NEAT Visual

Real Time NEAT Remove the worst adjusted fitness minion from population There must be an minion old enough Re calculate average fitness of each species Choose the Parent species that will produce the child Based on the probability of been choosen (Average fitness of species k)/ total species fitnesses Adjust compatibility threshold and reassign members Based on the number of species one wants Insert member in the environment.

Planning

AI in Space Group 3 Questions?