A Case-Based Approach to Knowledge Navigation

Kristian J. Hammond, Robin Burke, and Kathryn Schmitt

The AI Lab at Chicago has begun development of a new set of software agents designed to manage the flood of data colloquially called the "information superhighway". Our approach takes its lead from case-based technology [1,2] in that we are building systems that emphasize the use of examples over explicit queries or questions for communicating with the user. We propose to develop three sorts of systems: browsing systems (called FIND-ME systems), preference-based task organizers (called BUTLERS), and Internet news group agents (called CORRESPONDENTS). All three types of agents are designed to help a user navigate through an information space and either find or construct responses to fit the user’s needs. Two features distinguish these systems. The first is that they are derived from the case-based ideas Qf using retrieve and adapt as the core problem solving model. The second is that they use existing archives and data-bases as resources to be "mined" on demand rather than as fodder for batch processors that learn new concepts or construct new knowledge bases independently of a user. This too draws from the case-based philosophy of waiting until a problem arises to solve it. Our proposed efforts will investigate all three sorts of software agents. The focus of this proposal, however, will be on the FIND-ME systems and CORRESPONDENTS, the most developed of our projects.


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