Purpose‐Based Expert Finding in a Portfolio Management System
Xiaolin Niu, Gordon McCalla, Julita Vassileva · Computational Intelligence · 2004
Most of the research in the area of expert finding focuses on creating and maintaining centralized directories of experts' profiles, which users can search on demand. However, in a distributed multiagent‐based software environment, the autonomous agents are free to develop expert models or model fragments for their own purposes and from their viewpoints. Therefore, the focus of expert finding is shifting from the collection at one place as much data about a expert as possible to accessing on demand from various agents whatever user information is available at the moment and interpreting it for a particular purpose. This paper outlines purpose‐based expert modeling as an approach for finding an expert in a multiagent portfolio management system in which autonomous agents develop expert agent models independently and do not adhere to a common representation scheme. This approach aims to develop taxonomy of purposes that define a variety of context‐dependent user modeling processes, which are used by the users' personal agents to find appropriate expert agents to advise users on investing strategies.