5.5Example of an (unobtrusive) Agent Application: The Remembrance Agent
Most of the current information retrieval applications concentrate on query-based searches. However, they do not help when someone does not remember enough to even know to ask a question, or what question to ask;
The Remembrance Agent ([RHOD96]) performs this continuous, associative form of recall by continuously displaying relevant information which might be relevant to an individual user in this person’s current context. However, it does not just display this information, as this would distract the person too much and would not be of much help to him or her at all! The philosophy behind the Remembrance Agent (RA) therefore is that is should never distract from the user’s primary task, but only augment it. It accomplishes this by suggesting information sources (deemed possibly relevant to the user’s current situation) in the form of one-line summaries at the bottom of the screen or current active window. Here, they can be easily monitored (at an eye’s glance) without distracting from the primary activity the user is engaged in. The full text of a suggestion can be brought up with a single keystroke. Effectively, the agent thus becomes an augmentation of the a person’s memory. It is important to note that - unlike most information retrieval systems - the RA runs continuously without user intervention.
When looking for relevant and useful documents, the agent does not have very much information (e.g. context) it can use to perform its searches. As a result, many of the suggestions of the RA or false positives, and not all that useful. However, as there are almost no ‘costs’ to the user to see a suggestion and ignore it if deemed not useful at the time, this is not as big a problem as one might at first think. The fact that no colour cues or highlights are used when displaying suggestions, and the fact that suggestions are displayed at regular intervals, contribute to the low ‘costs’ of checking them (they do not distract too much).
While using the system, it became clear that suggestions are much more useful when the document being suggested only contains one ‘nugget’ of information, and when this nugget is clearly displayed on its one-line suggestion:
Using personal email and documents turned out to be a good idea for other reasons as well. The most important reason being that these are pre-personalised to each individual user and that these files will automatically change as the user’s interest changes. It turned out that this compensated very well for other RA shortcomings (as mentioned earlier on).
In future applications of the RA, it is planned to provide the back-end with an algorithm which learns based on which suggestions the user asked to have displayed. In this way, the RA can display more documents that are actually useful, and cull out those which seem to be useless suggestions.