POKEMON HACKS. Jodie Ashford Josh Baggott Chloe Barnes Jordan bird

Transcription

1 POKEMON HACKS Jodie Ashford Josh Baggott Chloe Barnes Jordan bird

2 Why pokemon? 1997 The Pokemon Problem an episode of the anime caused 685 children to have seizures Professor Graham Harding from Aston University was flown to japan He created a set of rules for tv that prevented this from happening again in the future What a legend

3 Part #1 Machine learning on brain activity to predict whether someone is watching pokemon

4 Since all conscious human activities are triggered in the brain, we can use EEG to discern what a person is: Thinking Doing Feeling Muse EEG HEADBAND

5 60 seconds watching pokemon 60 seconds watching a tutorial on Microsoft word from 1978 (it was very boring) Recorded 60 seconds of each state for 4 people 120 seconds per person = 8 minutes of raw data

6 Brainwave data is dynamic and temporal, but we need static data Time windowing technique Data point = the statistics of a time window

7 Temporal Extraction of 0.5 second windows of EEG data, every 0.25s interval

8 Statistics are extracted from each time window to produce one row of data Mean value 0.25s and 0.5s window Max value 0.25s and 0.5s window Min value 0.25s and 0.5s window Standard deviation Statistical moments 3rd, 4th order Mean value distance Max value distance Min value distance Log-covariance Shannon entropy Log-energy entropy Accumulative energy features

9 We used a premade script to do this part! 57 Megabytes! Watching Or Not watching (CLASS) attributes for each time window The dataset was huge

10 USING THIS DATA WE TRAINED VARIOUS MACHINE LEARNING MODELS For this we performed machine learning on the google cloud platform Naïve Bayes Bayesian Network J48 Tree Java implementation of the C4.5 Algorithm Random Tree Random Forest MLP NEURAL NETWORK SUPPORT VECTOR MACHINE (SVM)

11 RESULTS Model Prediction Accuracy (%) Naive Bayes Bayesian Network J48 Tree Random Tree Random Forest MLP Neural Network Support Vector Machine 71.76

12 But JUST how accurate is it? We record data at 150hz (150 times per second) At 94.07% this means that in 10 seconds of recording, we misclassify only around half a second WE. CAN. READ. YOUR. MIND.

13 NOTE: SINCE THE FRONTAL LOBE (AF7, AF8) is CLOSELY RELATED TO EMOTIONs, WE ARE ACTUALLY PROBABLY LEARNING TO CLASSIFY A POSITIVE EMOTIONAL EXPERIENCE

14 Part #2 Designing a Pokemon game with Machine Learning

15 Generating images of Pokemon Sprites from various Pokemon games were compiled into a large image dataset

16 Generating images of Pokemon A Deep Convolutional Generative Adversarial Neural Network (DCGAN) trained on this data for 3 hours and created new images inspired by what it had seen

17 How does a DCGAN work? VS. 1. Team Rocket forge a painting 2. Professor Oak learns to spot their forgery 3. Team Rocket have to learn produce a better forgery 4. Professor Oak has to learn to spot a better forgery 5. Repeat

18 How does a DCGAN work? VS. 1. Generator forges a image 2. Discriminator learns to spot the forgery 3. Generator has to learn produce a better forgery 4. Discriminator has to learn to spot a better forgery 5. Repeat

19 Generating images of Pokemon Generation 1 to 200 (3 hours of learning)

20 Generating images of Pokemon The process worked quite well and made great progress, but it would take a lot longer to generate Pokemon with more discernible features

21 Generating names and descriptions of Pokemon We fed the whole Pokedex (names and descriptions) to Long Short Term Memory neural networks to generate new text

22 Some examples of the horrible abominations we made Mowirup They flock to the stars and mountains. This Pokemon glows, it does not construct silk. Bigabble Very powerful, it absorbs filthy atmosphere. They check the air. It nurses the food colony. Rampant birds. Moomstu It chews to show strength. It starts rivers to sleep. Capable to twitch coughing bouncy gas. Shock and unknown fangs hide in the tree.