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Task-based Information Distribution Environment

Principal Investigator:  Michael J Wolverton

Mailing address:
AI Center
SRI International
333 Ravenswood Avenue
Menlo Park, CA 94025-3493

AIC Program:  Representation and Reasoning

   Project Description

The TIDE project's goal was to facilitate timely and selective information access by developing tools that automatically disseminate and organize documents based on relevance to models of collaborators' tasks. These tools were developed with two goals in mind: first, improved utility of delivered documents, because they are selected according to their value added to a user's current task(s); second, reduced burden on the user during the information delivery process, supporting effective dissemination of information in situations involving time pressure. To support these practical objectives, the technical objectives of the project were new representational formalisms for task modeling, a technique for deriving document queries from higher-level descriptions of information needs, and methods for adaptation of relevance metrics based on relevance feedback from the user.

The TIDE information management system works by matching documents to user task representations, as follows. Each user interacts with an Information Delivery Module (IDM) that runs on his machine. The IDM allows the user to select tasks from the system's task model and to instantiate those tasks to reflect the specific current situation. The IDM then computes a document query from the instantiated task model and sends that query to an information retrieval engine, which uses its similarity metric to compute a match between incoming documents and the query. Documents matching the query are displayed to the user in a document browser.

TIDE's method of deriving information needs (queries) from task descriptions is based on a model of the probabilistic relationships between tasks and their subtasks, and the relationships between tasks and keywords that occur in documents relevant to them. A key element of the method is an original approach to computing the relative degree of importance of one task to another. The components of the overall model that TIDE uses to match documents to task descriptions---specifically, the probabilities that relate tasks to one another---can be difficult to get exactly right the first time. To address this issue, the TIDE investigators have developed a model for adapting the probabilistic relationships between tasks, based on relevance feedback---input provided by the user about the relevance of documents delivered by the system. The adaptation model is consistent with TIDE's probabilistic method of deriving information needs. A user's relevance feedback causes the system to modify its beliefs about tasks' importance to one another, and that in turn causes the system to change the probabilities relating tasks. The adaptation approach allows us to have a system that is constantly improving the quality of its information delivery.

An important strength of TIDE's task representation and query derivation methods is that they support changing the criterion for document relevance to a task as a user learns and draws conclusions about the domain. When the user describes to TIDE how his belief in the answers to subquestions has changed based on the evidence seen so far, the query derived for the current task is modified to reflect these new beliefs. Viewed more broadly, TIDE's approach recognizes that information gathering occurs in a context---a context in which the user is continually digesting information and reaching conclusions based on it---and allows the information gathering process to take that context into account.


Name Title E-mail
Wolverton, Michael J Senior Computer Scientist

  • Wolverton, M. Task-based Information Management. ACM Computing Surveys, vol. 31, no. 2es, June 1999.  [PS, Details]

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