Human behaviour is remarkably complex. Even a simple request like, “Put the ball close to the box” still requires deep understanding of situated intent and language. The meaning of a word like ‘close’ can be difficult to pin down – placing the ball inside the box might technically be the closest, but it’s likely the speaker wants the ball placed next to the box. For a person to correctly act on the request, they must be able to understand and judge the situation and surrounding context.
Most artificial intelligence (AI) researchers now believe that writing computer code which can capture the nuances of situated interactions is impossible. Alternatively, modern machine learning (ML) researchers have focused on learning about these types of interactions from data. To explore these learning-based approaches and quickly build agents that can make sense of human instructions and safely perform actions in open-ended conditions, we created a research framework within a video game environment.
Today, we’re publishing a paper and collection of videos, showing our early steps in building video game AIs that can understand fuzzy human concepts – and therefore, can begin to interact with people on their own terms.
