Barbara Barry

Beating Common Sense into Interactive Applications

A long-standing dream of artificial intelligence has been to put commonsense knowledge into computers -- enabling machines to reason about everyday life. Some projects, such as Cyc, have begun to amass large collections of such knowledge. However, it is widely assumed that the use of common sense in interactive applications will remain impractical for years, until these collections can be considered sufficiently complete and commonsense reasoning sufficiently robust. Recently, at the Massachusetts Institute of Technologys Media Laboratory, we have had some success in applying commonsense knowledge in a number of intelligent interface agents, despite the admittedly spotty coverage and unreliable inference of todays commonsense knowledge systems. This article surveys several of these applications and reflects on interface design principles that enable successful use of commonsense knowledge.

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.

Documenting life: videography and common sense

This paper introduces a model for producing common sense metadata during video capture and describes how this technique can have a positive impact on content capture, representation, and presentation. Metadata entered into the system at the moment of capture is used to generate suggestions designed to help the videographer decide what to shoot, how to compose a shot and how to index their video material to best support their communication requirements. An approach and first experiments using a common sense database and reasoning techniques to support a partnership between the camera and videographer during video capture are presented.

ComicKit: acquiring story scripts using common sense feedback

At the Media Lab we are developing a resource called StoryNet, a very-large database of story scripts that can be used for commonsense reasoning by computers. This paper introduces ComicKit, an interface for acquiring StoryNet scripts from casual internet users. The core element of the interface is its ability to dynamically make common-sense suggestions that guide user story construction. We describe the encouraging results of a preliminary user study, and discuss future directions for ComicKit.

The mindful camera: common sense for documentary videography

Cameras with story understanding can help videographers reflect on their process of content capture during documentary construction. This paper describes a set of tools that use common sense knowledge to support documentary videography.