Week 1: Human-AI Teaming Isn't Only for Big Hero 6

Hello and welcome to my senior project blog. Today, I will discuss the background and key questions of my project, as well as the next steps in my research. 

Background

AI-human teaming exists in many forms across different fields and applications—for instance, the way you might interact with a Roomba differs from the way you might interact with Siri. In general, human-AI teams can be classified into one of two categories: “balanced” and “unbalanced.” Balanced human-AI teams involve a relatively equal amount of control given to both parties, while unbalanced human-AI teams feature one party occupying a significantly greater role in accomplishing the task. 

Since the majority of human-AI teaming is unbalanced (think self-driving cars and crop-harvesting robots), I decided to focus on investigating balanced human-AI teams. Despite being less common today, balanced human-AI teaming has potential to revolutionize a number of fields where unbalanced teaming would not work. A prime example of this is the finance and investment industry: many investors may be averse to the idea of completely entrusting their funds to an AI, but could benefit greatly from an AI assistant giving them suggestions about where and when to invest. To maintain this particular structure of balanced human-AI teaming (AI offering suggestions to a human decision-maker), I am basing my project on the decision-making game that my team and I created at MIT Beaverworks this summer. 

Experiment Design + Hypothesis

My initial game simulates an ambulance driver in a zombie apocalypse, whose objective is to squish zombies and save humans in order to gain points. Each time a humanoid appears, the player can take several different actions: skip, squish, save, and scram (return to the hospital). Squishing humans, saving zombies, and skipping injured humans will deduct points. The actions each take up a given amount of time, shown on the in-game clock, and the round ends once a given amount of time (e.g. 12 hours) has passed. Players can store a maximum of 10 humanoids in their ambulance, and the ambulance will be emptied upon a return to the hospital. 

For my project, I plan to add an AI feature that is called using a button in the game’s UI. When a new humanoid appears, the player can choose to click the button in order to see how valuable the AI considers the humanoid (from 1 to 10, 10 being the most valuable). I expect that this AI feature will increase the average player score compared to human-only trials. 

Next Steps

To begin my project, I will be creating a simpler version of my game as an OpenAI Gym environment and using it to train a Q-learning agent. Once the agent has been trained and tested successfully, I will repeat this process using a different environment that is directly connected to my game (so that the observation space is derived from the current game state). 

Thank you for reading and let me know if you have any questions. See you next week!