Computers are very good at storing data and performing repetitive functions very quickly. Humans, on the other hand, can be very good at intuitive leaps and recognizing patterns and structure, even when passive. Thus, an interface where the wearable computer helps the user remember and access information seems profitable. In general, 95\% of computer time is dedicated to word processing. With such convenient access to a keyboard, this percentage may be even higher for the wearable computer. However, word processing requires about 1\% of the processing power of the system. Instead of wasting the remaining 99\%, an information agent can use the time to search the user's personal text database for information relevant to the current context. The names and short excerpts of the closest matching files could then be displayed. If the search engine is fast enough, a continuously changing list of matches could be maintained, which would increase the probability that a useful piece of information will be recovered. Thus, the agent can act as a memory aid. Even if the user mostly ignores the agent, he will still tend to glance at it whenever there is a short break in his work. In order to explore such a work environment, the Remembrance Agent was created.
The benefits of the Remembrance Agent (RA) are many. First, the RA provides timely information. If writing a paper, the RA might suggest relevant references. If reading email and scheduling an appointment, the RA may happen to suggest relevant constraints. If holding a conversation with a colleague at a conference, the RA might bring up relevant work based on the notes taken. Since the RA ``thinks'' differently that its user, it often suggests combinations that the user would never put together. Thus, the RA can act as a constant ``brain-storming'' system.
The Remembrance Agent can help with personal organization. As new information arrives, the RA, by its nature, suggests files with similar information. Thus, the user gets suggestions on where to store the new information, avoiding the common phenomenon of multiple files with similar notes (e.g. archives-linux and linux-archives). The first trial of the prototype RA revealed many such inconsistencies on the sample database and suggested a new research project by its groupings.
As a user collects a large database of private knowledge, his RA becomes an expert on that knowledge base through constant re-training. A goal of the RA is to allow co-workers to access the ``public'' portions of this database conveniently without interrupting the user. Thus, if a colleague wants to know about augmented reality, he simply sends a message to the user's Remembrance Agent (e.g. thad-ra@media.mit.edu). The RA can then return its best guess at an appropriate file. Thus, the user is never bothered by the query, never has to format his knowledge, and the colleague feels free to use the resource as opposed to knocking on an office door. Knowledge transfer may occur in a similar fashion. When an engineer trains his replacement, he can also transfer his RA database of knowledge on the subject so that his replacement may continually receive the benefit of his experience even after he has left.
Possibly the most striking use of the Remembrance Agent is its ability to seemlessly share knowledge in a work group. Instead of simply using one member's notes, the database is expanded to include the members of a small work group. This allows personal experience to be shared quickly and conveniently. For example, such an interface is useful if one member of the workgroup is in charge of repairing the team's computers. When the team member receives new information about an obscure bug in the operating system, he puts it in his personal information files which can then is available to the rest of the team. If other members then experience this bug, the appropriate file might be suggested to the member. This helps identify the problem quickly and greatly reduces the overhead involved in diagnosing and assembling a well-formed question to the rest of the team, whose appropriate member may be unavailable.
To experiment with this idea, five volunteers have been assembled. These volunteers will pool their knowledge into one RA database which will run continuously in their text processor for a period of one week. To evaluate the efficacy of the system, logs will be kept as to which files were actually referenced based on the RA's suggestions.
The current Remembrance Agent uses the Savant information retrieval system developed in-house by the Autonomous Agents group. The Remembrance Agent runs through emacs, a popular text editor. The user interface is programmed in elisp, and the results are presented as a three line buffer at the bottom of the window. Several considerations have gone into the design of the RA. First, the RA should not be distracting unless unusual circumstances arise. To that end, the RA does not use boldface or highlighting and is run at a low priority. Secondly, if the RA recovers something of interest to the user, the full text is accessible with a quick key combination. Most importantly, the RA searches on local, medial, and global contexts. In particular, the RA searches on the last 10 words, last 50 words, and last 100 words and returns the results of these searches on the last, middle, and first lines of its text buffer respectively. These values are configurable due to different needs with different text databases. To conserve computer power, searches are run every 10 seconds.
Figure 1 shows the output of the Remembrance Agent. The reference database for the RA was the third author's e-mail archives. The first number on each line of the RA output is simply a file label for convenience. For example, to view message 2, the user would simply press ``Control-2'' The second number on each line refers to the ``relevance measure'' of the message.