Teaching Machines about Everyday Life

In order to build software that can deeply understand people and our problems, we require computational tools that give machines the capacity to learn and reason about everyday life. We describe three commonsense knowledge bases that take unconventional approaches to representing, acquiring, and reasoning with large quantities of commonsense knowledge. Each adopts a different approach — ConceptNet is a large-scale semantic network, LifeNet is a probabilistic graphical model, and StoryNet is a database of story-scripts. We describe the evolution, architecture and operation of these three systems, and conclude with a discussion of how we might combine them into an integrated commonsense reasoning system.