Abstract

In order to build a new breed of software that can deeply understand people and our problems, so that they can help us to solve them, we are developing at the Media Lab a suite of computational tools to give machines the capacity to learn and reason about everyday life---in other words, to give machines `common sense'. We are building several large-scale commonsense knowledge bases that model broad aspects of the ordinary human world, including descriptions of the kinds of goals people have, the actions we can take and their effects, the kinds of objects that we encounter every day, and so forth, as well as the relationships between such entities. In this article we describe three systems we have built---ConceptNet, LifeNet, and StoryNet---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 of these three systems, the techniques that underlie their construction and their operation, and conclude with a discussion of how we might combine them into an integrated commonsense reasoning system that uses multiple representations and reasoning methods.

Citations