Pattie Maes...Interacting with Virtual Pets...Doors2
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P r a c t i c a l U s e s f o r A g e n t s a s A l t e r E g o s
We've also been working on the problem of how agents can do useful things for us and be more than just virtual playmates. We've built a whole range of agents that perhaps aren't as interesting to look at or aren't fully computer-animated, but that do very useful things for people. All of the applications that we have been looking are motivated by our own frustration with the way we currently are dealing with certain tasks.
Tasks such as electronic mail. I'm not sure how many messages you get every day, but I get at least a hundred. And in a typical electronic mail system, they're just presented chronologically. So I decided it would be a good idea to try and build an agent that helps me with my electronic mail, by sorts, prioritising even marking certain messages to be deleted before I look at them (this is actually the most useful function).
Another such problem is calendar management and meeting scheduling. All of you know what it's like to schedule a meeting that involves more than two people. It takes forever to find a time when everybody can make it. I don't believe that current software like Meeting Maker solves the problem, because it takes away privacy. Other people can examine your agenda and say: you have a block of free time there, so I'll schedule a meeting over there! I don't find that an acceptable solution. We've been building a calendar agent which is rather like a user alter ego that can negotiate about good times to schedule meetings.
N e w T
I'll go into more detail about two agents that everyone would find really very useful. NUUT is a personalised newspaper agent that looks at news feeds, in particular Internet news, and recommends articles to be read. RINGO is a music recommendation system. We're also building an agent for the World Wide Web that will recommend documents to the user.
Apart from the World Wide Web agents, all of these agents are being used by people today. If you're interested in using some and getting the software, come to talk to me or send me electronic mail. My agent will delete it (only joking!).
The NewT system is actually a collection of agents which help the user decide what Net news articles to read.
In the NewT system, you can actually create a set of agents. I created four agents here. You can even make a little visual representation of them to remind you what kind of news they represent for you. There was a politics agent, a computer news agent and a couple of others. Each of these agents make representations to me on a daily basis or even more often, for example, every couple of hours for new articles from the Net news feeds (about four hundred megabytes of new articles every week).
Each of these agents makes recommendations to me based on what I've shown interest in in the past. These are actually examples of learning agents. They continually watch my behaviour and notice patterns. For example, they may notice that I read Michael Schrage's column in the LA Times every week. And once they have picked up a certain pattern like that, they can automate it on my behalf and offer me that column every week, so I don't have to search for it. This is a set of articles one of my agents has retrieved. These are articles suggested by the politics agent. I can click on one of these titles. The agent has ordered them according to how important it thinks they are for me. I can look at the article by clicking on the title and then give either positive or negative feedback to my agent, indicating that I want to receive more or less of that kind of news article in the future. I can also highlight an area of the article and say: this is what I want to know more about. Or I can highlight the author's name and say: don't give any more articles by this author. You interact with this agent by giving it positive and negative examples of things that you wanted it to retrieve. NewT uses a technique called content filtering: it notices correlations in the kinds of things I like and dislike.
R I N G O
RINGO (http://ringo.media.mit.edu/ringo/ringo.html) uses a complimentary technique called social filtering. Rather than finding correlations among the types of things that I like or dislike, this system actually tries to find correlations between the tastes of different users dealing with the same type of information. In this case, the system recommends music. This is a World Wide Web interface for which you currently need to have an MIT address, but which will soon be more widely available. Right now, if any of you want to try it, you can try accessing the system via address mail. The address is: ringo@media.mit.edu .
So you basically tell this system a little bit about what kind of music you like and dislike. For example, a user could say they like the Beatles a lot. The scale goes from one to seven. One means: I seriously dislike this music; seven means: I really love this music. This user has indicated liking the Beatles and not liking Madonna, among other preferences. Rather than trying to find correlations among all these different music albums, the system tries to find correlations between my data and that of other users who have conveyed to the system what they like or dislike. For example, this particular user's taste in music seems similar to mine, because that user also indicated liking the Beatles and disliking Madonna. Once RINGO has discovered which users have similar tastes, it will actually recommend that I listen to music that other people like me have liked (as here, for example, Eric Clapton here). It may also use low values to tell users that they will probably not like that type of music.
There are a lot of other neat features to the system. One of the neatest things is that it can improve by itself. We started this system with twenty users and 575 artists in the data base. Now, after two months without doing any advertising (this is the first time I've ever advertised it to a large audience), there's more than 3000 users in the database and more than 9000 albums. All of those have been added by the users, so RINGO recommendations are continuously improving because the more users there are in the system, the higher the probability is that there will be users in the system that have tastes similar to your own.
These are two examples of agents that perform useful tasks on behalf of the user. I've shown you some examples of how we envision interaction with these agents in the future. And the main message I hope you have gotten out of this lecture is that the vision we are working towards is one where the computer is almost a window or door to this virtual world populated by agents that assist you, entertain you, and even may train you in a very personalised, interactive and natural way. for more information check out http://agents.www.media.mit.edu/groups/agents/
Speakers | Conference Report | Doors 2 | Mediamatic Index
Last Updated: 23 feb 1995
© copyright Pattie Maes, 1994
