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2017 Spring AI Project – PoNNg

Towards the end of my first year at Virginia Tech, I oddly found myself with more free time than usual. Throughout the year I tried to focus on my neural network study when I could, but sadly, little time arose for me to pursue what I love. However, I always worked on small projects when I could. The majority of this time was spent geared towards learning Linux and Python, both of which I have become somewhat proficient in. Using these newfound skills, I looked back to my roots to see what improvements I could make on my original code from high school and to get back into the field of artificial intelligence in general. The main goal for this project was to create a short, dependable neural network algorithm for personal use in Python. I also wanted to create a system in which 2 neural networks were pitted against each other in some form of competition. I thought pong was a suitable game for this purpose because of its simplicity and competitive nature. My version of it stays, for the most part, true to the original game. 2 paddles, 1 ball, reset and score increase upon the ball exiting the left or right screen. After each bounce, the ball’s y velocity is randomly chosen between [2, 5] to add some randomness to where the ball will be when it reaches the left or right side of the screen. Pygame was used to render the components onto a window.

The general frame of mind I had while creating the neural network algorithm for this project was brevity and simplicity. One of the main issues with my Java implementation from a year ago was the fact that all of the weights were stored in a single 1D array, which isn’t ideal for many reasons. Mainly, it’s confusing to look at and understand the math behind extracting the correct weight at the correct time. To correct this, I switched it from a 1D array to a 3D array, in the format of [layer][input][output], layer being the current index in regards to the hidden layer, input being the “left” set, and output being the “right” set (i.e. input -> hidden, input is the “left” set and hidden is the “right” set). Although confusing at first due to my lack of experience with 3D arrays, I eventually came up with a good system that works just as fine as the original and is slightly easier to understand from an outsiders point of view. e

Screenshot from 2017-04-09 19-13-31

Example of a 3D weight storage system for a neural network with 2 input nodes, 1 hidden layer of 3 nodes, and 1 output node. The number of rows corresponds to the “input” layer (input -> hidden; input is the input layer, hidden -> output; hidden is the input layer) and the number of columns corresponds to the “output” layer (hidden and output in the above example).

In this project, I experimented around with neural networks of varying sizes before settling on a simple 2 node input, 3 node hidden layer, and 1 node output network. The input was the vertical and horizontal distance between the paddle and the ball over the total height/width of the game window. When given more input nodes like the x and y velocity of the ball, it was observed that the neural networks were able to accurately predict where the ball would hit the paddle when it reached the side of the window (as opposed to just following the ball’s y component), but the time it took to reach this point was incredibly long and results varied over different situations (i.e. the angle the ball was approaching the paddle, etc.). Fitness was given based on how many matches the neural network won, but was punished twice as hard for every match (first to 3 points) lost (+1 for win, -2 for loss). A standard breeding process was performed after each match in which the parents were the 2 best performing networks in a population and the child was the worst, with the child weights having 2/3 chance of being from either of the parents and 1/3 chance of being the average of the parent weights. There was a 6% chance of any weight being erased and assigned a random value between (-1, 1). After each match, the loser was replaced by a randomly selected network from the population (it was ensured that the two networks playing each other were not the same one). Each paddle’s neural network can be seen displayed on its side of the board — each circle being a node and each line being a weight. A blue weight signifies a negative value and a red weight signifies a positive value.

Getting on to the actual project, the rate at which the neural networks became “good” at the game varied on each run. In my experience, it could be as short as 100 breeds, and as long as around 2000. However, for each run, the neural networks followed the same general pattern every time — make no attempt to move for the ball, show some attempt at chasing the ball or “reacting” the ball when it came close, and successfully chasing the ball.

before

Right when the program started. You can see the paddles move to either the top or the bottom of the screen, paying no attention to the ball.

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Networks show some learning — they now react to the ball when it approaches them, though they do not move to the correct y value as of yet.

after

The end of the pattern. Networks demonstrate ability to chase after the ball and hit it every time. At this point, the match time is indefinite.

Although this project seemed simple at first, there were many different stages it went through while in development. At first, the input size for the neural networks was complex (including paddle location, ball location, ball velocity). However, after a lot of playing around, consistent improvement was shown when this list was refined to just the distance between the paddle and the ball. In retrospect, this makes sense, as decreasing the complexity of any problem makes it easier to solve. Although, maybe with more patience, a more unique strategy could have been developed with more inputs. I encourage you to download my source code and play around with yourself if you are at all interested in doing so (here).

2016 Spring AI Project – Floppo Bordo

As my senior year of high school began to slow down and the college hype began to increase, I looked for simple projects to work on while in the midst of the craziness. I spent a while sitting back and looking at the projects I’ve finished this past school year and remembered a couple of other projects from other people that served as inspiration for my personal projects. Since I learned everything I know about AI from watching lectures and demonstrations online, it was a trip down memory lane to go back and watch those videos in particular. One of the projects I looked back at was a machine learning algorithm that used reinforcement learning for Flappy Bird (A popular mobile game), and I remember being amazed at the actual process of learning that the algorithm went through (see that video here). I decided to challenge myself by making my own Flappy Bird AI to benchmark my current progress, especially in comparison to where I was 6 months ago.

First, however, I had to make a Flappy Bird clone. Making it was surprisingly easy, as it has simple rules and doesn’t require a lot of complex algorithms. Flappy Bird is essentially an endless runner, but the player must tap the bird to fly precariously in between pipes (if the player touches one of these pipes they die and have to start over). Since this was just a quick project, I didn’t bother with making images to go over each of the objects, instead using colored rectangles. The blue rectangle is the player, the black rectangles are pipes, and the green rectangle is the ground.

I used my standard Neural Network algorithm with a genetic algorithm for the machine learning AI (read more about Neural Networks here). It uses 4 inputs – the player’s y position (height from the ground), the y positions of the opening between the closest pipes (the lowest point on the top pipe and the highest point on the bottom pipe), and the horizontal distance between the player and the closest pipe. There are 2 hidden layers of 5 nodes each, and 1 output node (if the output node > 0.5, the bird jumps). I used a sigmoid function (1 / (1 + e^-x)) as my activation function. Reward was based on the score at the end of the run and how far the AI moved the bird to the right.

Now onto the results. It actually learned to jump between pipes fairly quickly, but difficulty came when it needed to jump with precision between the pipes. However, after a little bit of time, it mastered the game perfectly.

start

Start of the simulation. You can see the AI has trouble handling its own jump height.

stend

After about 10/15 minutes of runtime, the AI has mastered the art of jumping in between the pipes.

I enjoyed making this project and seeing how far I’ve come. A while ago, I never would have thought I would have been able to make something like this. If you want to see the code for this project, you can get it here.

2016 Spring AI Project – TicTacToe & Brute Search

As my senior year of high school began to slow down and the college hype began to increase, I looked for simple projects to work on while in the midst of the craziness. Looking at the history of Artificial Intelligence, I began to read up on the different methods AI developers used before modern AI existed (Neural Networks would be an example of a modern day technique). I also decided to code this project in Python in order to broaden my skills with other languages (and because Python is pretty fun to use).

Artificial Intelligence for games like TicTacToe and Minimax are prime examples of early stage AI – programs that brute search their way through an entire list of possible situations in order to find the best action to take. However, brute searching only works on games with a small amount of game boards. For instance, Minimax is a really simple game because the amount of choices and paths players can take is very limited. TicTacToe ramps it up a little (about 9! or 362,880 different ways to play the game), but is still feasible with a little bit of load time. Games like chess, however, are almost impossible to brute search just from the sheer amount of games possible (about 10^10^50 games, in fact, there are more games of chess than grains of sand on the Earth). This means that although brute searching is an alright method to use when creating game AI, it isn’t always the most efficient, and other options should be considered the more complex the game is.

One of the main roadblocks I hit along the way on this project was dealing with recursion. I am familiar with recursion and its usefulness, but for more complicated tasks like this, it can become confusing at times (especially writing in an unfamiliar language, might I add). For those who don’t know much about recursion, it’s essentially a function that calls itself in order to simplify a problem. It’s complicated in that its formation is very abstract and you have to keep some key issues in mind while creating recursive functions (i.e., making sure you don’t accidentally create a function that loops indefinitely). After some frustration / hair pulling out, I was able to overcome the challenge and write a program that performs pretty well. Basically, the AI looks at each possible choice it has and finds the probability it will win if it chooses that path taking into consideration all possible game boards extending from that board. After probabilities for each possible spot to play are calculated, the AI chooses the option with the highest probability of success.

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Here the AI is controlling Xs and I am controlling Os. You can see the AI was successfully able to perform a double attack, forcing a win for Xs.

This project was fun to make. Comparing it with the Neural Networks I’ve made this year, It’s amazing to see how far Computer Scientists have come in terms of Artificial Intelligence and the ability for computers to recognize patterns and solve problems. If you want to see the code for this project, you can get it here: https://github.com/mccloskeybr/tictactoe

2016 Spring AI Project – LD33 & Neural Networks

As my senior year of high school began to slow down and the college hype began to increase, I looked for simple projects to work on while in the midst of the craziness. Thinking that it had been a while since I had made anything relating to artificial intelligence, I wanted to refresh my mind on Neural Networks and machine learning in general. Since I needed a game to attach an AI to, I naturally thought back to my Ludum Dare 33 game (linked here), as it was a rather simple game that wouldn’t be hard for a computer to master.

I used a Neural Network (read more about Neural Networks here) with 2 input nodes (one to tell when an arrow was close and another to tell when a house was close), 2 hidden layers of 5 nodes each, and 4 output nodes (controlled each of the 4 keys required to play the game: punch, block, jump, move right). The activation function was again a sigmoid (1 / (1 + e^-4.9x)). I used a genetic algorithm with a 10% mutation rate on each gene passed down in order to weed out bad Networks and incentivize growth of good Networks.

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Graphic displaying the structure of the Neural Network I used for this project. Inputs were either a -1 or a +1 depending on whether or not its specific object was close (-1 for false, +1 for true). A key was pressed if its respective output value was > 0.5 (the range for the sinusoid I used was (-1, 1)).

Despite my belief that my game was easy, initial runs of the simulation proved that although scoring points was easy, there were too many ways to earn points, which confused the networks. The Networks easily figured out that moving right was a good way to earn points as points were given based on how many civilians are squished, but it had a hard time figuring out that it could squish civilians longer if it blocked arrows. However, after many many iterations, Networks could generally figure out a better strategy for scoring (whether it be jumping on houses to destroy them or blocking arrows). Despite the Networks learning how to score points better, I have yet to see a perfect strategy formed (one that jumps on houses AND blocks arrows), which may be solved if I ran more iterations of the simulation.

ld33aistart.gif

Starting out you can see that it doesn’t do anything. Usually, if it does anything, it holds down random keys, causing its actions to be sporadic at best.

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After ~5 minutes of the simulation running, the AI finds out that walking right is a good strategy for getting points.

ld33aiBLOCK.gif

After a considerably long time, the AI has figured out blocking is a good combo with walking, as it can walk longer distances than if it wasn’t blocking. However, it hasn’t found out that destroying houses is a good source of points.

All in all I think this was a great refresher for AI. Since I’ve been mostly working on games these past few months, it was also a good break from the grind that asset creation is becoming. Also, learning that machines aren’t always as bright as I may think they are is a great lesson to take going forward in my AI career. If you want to see the code for this you can see it here: https://github.com/mccloskeybr/LD33

2016 Spring Game Project (In Progress) – Adventures of Mark

A lot has happened since my previous update nearly a month ago. The main area of my focus has been switching from a top down view to something more isometric (think games like Earthbound), and the perspective challenges that are associated with that. I’ve also added a lot of new objects, tiles, and items (including various shops). Making this game has really opened my eyes to just how many assets and things of the like are required for not just indie-games like this, but professionally done AAA titles as well, and how difficult and tedious asset creation can be at times.

One of my more recent undertakings has been overhauling the way I create levels in the game. Before, I used a system of images where each pixel was a tile in the level, which was tedious and inefficient for a variety of reasons. Memory wise, it required a relatively large amount of effort to load each level. Making the levels themselves was also difficult because I had to use a specific set of colors with precise R, G, and B values to get the desired outcome, and didn’t get to see what the final outcome would look like until I loaded it into the game. Now, I use a level editor that I made myself that really facilitates level making because you can actually see the tiles you’re placing instead of just solid colored pixels.

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Although it is a simple editor, it has everything I need to make levels quickly and efficiently. The lighter tiles are floor tiles while the darker tiles are walls. The blank spaces are place holders while I create more tiles to be input into the game.

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The map I created inside the level editor loaded into game.

It did take a little while to complete the map editor, and I still have to add objects, but the benefits definitely outweigh the costs. Now, the tile IDs are saved to a text file that can be easily read and made into a tile map that’s loaded pretty quickly.

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The text file created by the editor to be loaded into the game. This particular text file creates the level shown above.

When I first thought of making a level editor, I thought the idea was a good one, but backed away because I wasn’t familiar with a lot of the window components needed to make one (for instance JButtons and JToolBars). However, I’m glad I worked through and figured out how everything meshes together because making it was fun and interesting, and I can make levels more efficiently from now on.

If you want to see the game as it progresses in detail, you can visit the github repo here: https://github.com/VolitionDevelopment/The-Adventures-of-Mark

2016 Spring Game Project – Plane Challenge

As my senior year of high school began to slow down, I began to look at more relaxing and laid-back  projects. Games, to me, are fairly low stress as there isn’t that much problem solving (at least at the early stages), so I decided to make a couple of games during my final semester of High School.

On my spring break trip to Grand Cayman, I decided I wanted to see how much of a game I could make on the plane ride down. This resulted in a fairly simple game that was completed in roughly 2 hours (give or take 10 minutes). Granted, I did start laying the ground work while I was waiting to board, but most of the work was done on the plane itself.

The game itself ended up being about planes. As I didn’t have too much time to work on it, I decided to make a simple endless runner in which the player dodged obstacles while trying to get as far as they could get without dying. The player, in my game, controls a plane that can move left and right, dodging debris while also picking up health packs.

Screen Shot 2016-03-06 at 11.38.42 AM

As you can see, I really whipped out my AP Drawing art skills on this one. In reality, I wanted to spend more time coding than on asset creation (which definitely shows in the final product).

One of the challenges of making this game was creating an endlessly looping background. The way I ended up doing it was having one image that was the size of the window (500 x 500 pixels) that was stacked on top of itself 3 times. The resulting picture scrolls down and when it hits a specific point (close to the end), it jumps back up exactly 1 image height and continues to scroll down, resulting in the illusion of an endlessly scrolling image.

If I had a bit more time to work on this project, I’d first add a menu, then a scoring system that went up the longer the player was alive.

I’m now going to start uploading all of my projects to Github, so exploring the code is a little bit easier. If you want to see the code for this project, you can see it here: https://github.com/mccloskeybr/PlaneProject

2016 Spring Game Project (In Progress) – The Adventures of Mark

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I’m currently working on a 2D Java game with inspiration coming from games like Final Fantasy and Undertale called The Adventures of Mark. It features a typical too-tired-for-this-shit college pizza delivery boy who must embark on an adventure down the block to earn his pay. Will his Tolerance and Brain Power hold true? Find out, nightly, on the GitHub repository featuring this project located here. This is definitely one of the larger games I’ve worked on and is filling me with more and more determination each day.

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Current state of the battle system. All of the assets (except the fonts) are drawn digitally and animated by myself.

I’m collaborating on the game with a friend of mine, Jackson Yeager (his portfolio). Although Jackson may have more knowledge than myself when it comes to various data structures, he has less experience with game making. Regardless, I’m sure both of us will learn something from this project, and I would definitely recommend you take a look at it if you’re at all interested.

I’ll be updating this post/making new ones as the game progresses.

2015 Winter Cryptography Project – P / NP, The Clique Problem

I recently read a book titled The Golden Ticket by Lance Fortnow in which the P / NP Problem and its importance to the theory of Computer Science are heavily discussed. Explained briefly, the P / NP Problem asks if every single problem can be solved quickly and efficiently. In relations to cryptography, P / NP is crucial because it allows for the creation of hard math problems to, for example, be able to hide private information on a public network. One of the most famous P / NP Problems, the Clique problem, was discussed and is, in my opinion, one of the more intriguing problems in the book.

Take, for example, a society called Frenemy, in which every citizen either has a 50% chance to be friends or a a 50% chance to be an enemy with a person they just met. In Fortnow’s book, he details how it would be virtually impossible to find all cliques of varying sizes (difficulty/time to find all cliques increases as size of desired clique increase) inside of this society (A clique is a group of people in which everyone is friends with all other members). At first, I didn’t think it would be that difficult to find the largest clique, but after making a simulation for myself, I was proven otherwise.

I stayed true to the Frenemy rules in my simulation – every time someone meets someone else, they have a 50% chance to either be their friend or their enemy. I experimented with varying population sizes as well as varying amounts of citizens in which each person can meet. I made two methods in order to experiment, one in which everyone meets everyone else in the society, and one in which everyone meets everyone on their own street (9 total streets in which 1/9 of the total population lives on each given street). To make the data easier to set up and understand, each citizen is given an identification number as well as an ‘address’, the number of street they live on. I aimed to find every single clique of size 3 in the society, and to see for myself how difficult it became to find them as population size increased.

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A couple of cliques present in a population size of 100 in which citizens met everyone on their own street.

I found that the total time to generate the population and find cliques of size 3 took much much longer the higher the population size was, which makes sense in hindsight. Adding a single citizen to a population size of 20 where everyone meets everybody doesn’t have nearly the impact as adding a citizen to a population size of 10,000,  as the number of updates a computer has to make to each citizen’s list of friends and enemies for a population of 10,000 is much larger. When I was reading Fortnow’s book, I didn’t put much thought into the rate at which the updates change as population size grows. I naively thought growth was generally linear, allowing total computations to find all cliques of size 3 to be relatively small compared to the actual value. Only after I made my own simulation did I truly understand the difficulty surrounding finding a solution to the Clique problem. If you find find yourself with some free time, I would recommend making a simple simulation like this for yourself so you can see the results firsthand.

I’d like to thank Lance Fortnow for pushing me to explore this problem through his book, as all P / NP problems (not just the Clique problem) are profoundly interesting in their own rights. And, if you’re planning to look more into the P / NP problem, Lance Fortnow’s The Golden Ticket is a must read. The discussion as well as the existence of the P / NP Problem has really expanded my understanding of Computer Science and the importance of not only the physical act of coding, but the theory behind CS as well.

2015 Winter AI/Arduino Project – Brainy Crawler

Having taken a general engineering course my senior year of High school at the same time I was researching Neural Networks (read more about Neural Networks here), I decided it was time to make a physical agent – a tangible robot controlled by a Neural Network. My background in robotics helped in this venture as well, as I knew before hand how servos and ultrasonic sensors worked.

Before getting to the hard part of writing the code to control the robot, I had to make the agent itself first. I knew I wanted to make a robot that learned how to move, but I didn’t have 2 motors. Instead, I decided to use servos, as it not only allows movement, but the motion itself is complex and warrants a fair amount of learning in order to master it. For parts, I used an Arduino Uno with 2 Parallax standard servos and 1 HC-SR04 Ultrasonic sensor. A friend of mine, Clay Busbey, helped out by 3D printing parts for me. I had 3 parts printed, the main chassis and 2 essentially straight bars used as 1 arm with a joint. I put the Arduino Uno and a half+ breadboard onto the chassis and hooked everything up from there.

crawler_png

Fritzing diagram of how I hooked up all of the components. It doesn’t show the bars used for arms, but you’ll see how that works in the next gif. The red and green LEDs indicate whether the Arduino is switching between Networks, red meaning it’s switching, and green meaning movements are controlled by a Neural Network.

In terms of the Neural Network, I used 3 input nodes, 1 hidden layer of 10 nodes, and 2 output nodes. The input values are the current position of each servo, along with the current distance away from the object detected in the ultrasonic sensor. The output values decide whether the servo should increase or decrease its current angle (> or < 0.5). I used a sigmoid function (1 / (1 + e^(-x))) as my activation function. Each Neural Network controls one motion, with a genetic algorithm that replaces the least successful Network (2 best networks make a crossover/mutation child that replaces worst network). Fitness is distributed based on how far the robot moved while under control of each Neural Network.

brainy crawler

Timelapse of the agent moving across the floor (sped up 3x). You can see that although it has a good start, it hiccups due to either a bad breed or mutation.

I enjoyed working on this project. It’s really one thing to watch a program learn in the real world as opposed to in the virtual world.

If you want to get the Arduino script and/or the .stl files for 3D printing the parts, you can get them here (.zip, 15 kb): https://drive.google.com/file/d/0Bz_0wgRmDpKqdGdGUS1YMk9oN1E/view?usp=sharing

2015 Winter AI Project – Evolution Simulator

The 2015 school year was when I began dabbling around AI. The idea always amazed me. Everything from movie depictions of them to the real practical uses they have in modern day life was interesting, and I wanted to know more about it.

Wanting to explore Neural Networks deeper (read more about Neural Networks here), I decided to make an evolution simulator. The aim of the project was to create a population of creatures that, starting with knowing nothing about their environment, evolved to chase after food and run away from predators. The creatures can also evolve different values of attack and defense, of which determines if that creature can eat other creatures. A set of 12 creatures is allowed to survive for as long as it can (must eat food to replenish a diminishing energy value, if it gets to 0 the creature dies), and at the end of the generation, the bottom half is eliminated, the top half reproduces sexually once (2 children) with each other (first mates with second, third mates with fourth, and so on), and asexually once as well. Reproduction is accomplished with crossover/mutation, that takes into account the weights for each creatures Neural Network and the attack value for each creature. Fitness is distributed based on how many times the creature eats.

The Neural Network itself was fun to make. I decided I wanted to make an adaptable system, that is, one in which I can change whenever I want it to. This includes the size of each individual layer and the total amount of hidden layers. For this project, I got the most success using one and two hidden layers with different amount of nodes for each (18 nodes for 1 layer, 5 nodes for 2 layers).

The input for each creature is as follows: 2 ‘eyes’ that take up 4 input nodes, as well as one node expressing the current angle the creature is facing. Each eye detects the angle from the creature to the closest entity (or second closest, for the second eye) and the r, g, and b values of that entity, expressed as a fraction over 255 (the range a color value can have is [0, 255]), allowing the creature to evolve decision making based on whether the entity it sees is edible or not. The output nodes control whether it turns left or right, and its velocity forwards or backwards (move more slowly while going backwards). I used a sigmoid function (1 / (1 + e^(-x))) as my activation function.

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Neural Network that has 9 input nodes, 1 hidden layer with 18 nodes, and 2 output nodes. The color for each weight is explained below.

Before I go over examples of the simulation, I’ll explain what each of the graphics in the frame represent. The creatures are represented by shades of purple (creatures that have a high defense are more blue while creatures that have a high attack are more red), with the current best creature (highest fitness) is outlined in yellow. The line extending from the center from each creature to its outline is the direction in which the creature is currently facing. Food is represented by the green squares. Moving onto the panels outside the main frame, the one in the top right shows the current generation, the amount of creatures alive (from a set max population size of 12), the best fitness value from the overall simulation, the best fitness value from the previous generation, and the best fitness for the current generation (represented with O, LR, and C respectively). The graphic below that panel displays the Neural Network of the creature that garnered the best fitness, where connections that are mostly red are between (0, 1), and connections that are mostly blue are between (-1, 0). Finally, the graph displayed on the bottom of the frame illustrates the best fitness for each round and shows overall growth of the population.

beginning

At the beginning, you can observe how most creatures are spinning out of control and lack in objective.

structured

After some time, you can see that although the creatures have not evolved the ability to go directly towards food, they have formed some structure in the way they collect it. You can also see that this population has evolved to contain creatures that have a high defense, as creatures that had a high attack in previous generations did not perform as well.

end usual

The usual ending. Creatures have evolved to not only go towards food, but to distinguish food from other creatures. That means that although an entity might be closer and thus more efficient to go towards, the creatures recognizes that entity as another creature and decides to go towards an entity that is further away because it recognizes that as food.

ending weird

Although creatures tend to evolve the ability to go straight for food, there are other strategies that have the possibility to emerge. For instance, in the simulation above, the creatures have come up with the strategy to propel themselves vertically, turn horizontally, and propel themselves the left or right whenever they detect food.

This project was incredibly fun to make. Although I had some frustration at the start while learning how to make Neural Networks of varying size, it  was fun finding solutions to my problems. I will probably keep working on this project for weeks or months to come, and I will definitely be utilizing my adaptive Neural Network on future projects.

If you want the source code, you can get it here (Java, 35 kb): https://drive.google.com/file/d/0Bz_0wgRmDpKqaXV0TVp0aXhIcTA/view?usp=sharing

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