Cognitive Personal Assistant

Computerworld - Researchers at Carnegie Mellon University are developing a computer-based administrative assistant that draws upon artificial intelligence (AI) techniques to perform routine tasks such as scheduling meetings for busy managers and filtering and prioritizing their e-mail.

One caveat: It won't pick up your dry cleaning.

The project, called Radar (short for Reflective Agent with Distributed Adaptive Reasoning), is being funded by the Defense Advanced Research Projects Agency under a program called PAL, or Personalized Assistant that Learns. DARPA provided the Radar project, which was launched in May 2003, with $7 million in first-year funding.

"What we're trying to do is build an assistant for any busy manager who's overloaded with requests," says Scott Fahlman, a research professor of computer science at Pittsburgh-based Carnegie Mellon. More than 25 researchers spent Radar's first year focused on things such as teaching the system to classify e-mail and then optimizing its learning algorithms.

According to Fahlman, Radar will handle some routine tasks by itself, ask for a supervisor's confirmation on others and produce suggestions and drafts that its user can accept or modify as needed.

For instance, suppose a manager receives an e-mail from a colleague requesting some slides. Fahlman and his team are trying to optimize the Radar system to understand the request at a basic level, draft a response and notify the manager with a message like, "Here's my proposed answer; do you accept this?" and then await the manager's response.

Radar isn't intended to act just as an e-mail filtering system, Fahlman says. As a text-in, text-out system, there's "a huge opportunity" for one Radar system to "talk" with another Radar system, schedule meetings and draw information from or post it to a company's Web site, he explains.

But, Fahlman notes, any release of information by Radar is under control of the system's user, who has the last word on the privacy policies to be observed by the automated assistant.

Using AI, Radar will draw on statistical and symbolic learning. Say a manager demonstrates a tendency to deny e-mail requests to hold meetings on Fridays over the course of a few months. Radar will pick up on this pattern and send a message to the manager asking whether the manager prefers to avoid meetings on Fridays. The manager can then respond back to Radar that it should avoid scheduling meetings on Friday mornings but that Friday afternoons are OK, explains Fahlman.

"What we're trying to do is blend the best of both statistical and symbolic learning," he says.

Applying AI to natural-language understanding is hardly a new concept -- researchers have been working on this for at least 25 years, Fahlman notes. But much of the research has centered around problem-solving, and Radar is "trying to move that work forward," he says.