{murray lowrance appelt rodriguez} @ ai.sri.com The need for collaboration arises in many activities required for effective problem solving and decision making. We are developing Angler, a web-services tool that supports collaboration among participants on some focus topic. Angler overcomes some common barriers to collaboration by enabling asynchronous and distributed collaboration. Angler supports a collaboration methodology that exploits opportunities afforded by multiple participants each making contributions to the collaboration. One challenge that arises in helping participants manage their contributions and their review of others ’ contributions is determining when one contribution is very similar to another contribution. Two very similar contributions may suggest either a need to merge them or to further elaborate one or both of them. Indexes over the participant contributions are used to assess similarity across contributions and address this challenge. The indexes may comprise lexical or ontological information; the former indexes require fewer resources to deploy but the later appear to support better similarity estimates.

Eliciting complex logical rules directly from logic-naive subject matter experts (SMEs) is a challenging knowledge capture task. We describe a large-scale experiment to evaluate tools designed to produce SME-authored rule bases. We assess the quality of the rule bases with respect to the: 1) performance on the addressed functional task (military course of action (COA) critiquing); and 2) intrinsic knowledge representation quality. In the course of this assessment, we note both strengths and weaknesses in the state of the art, and accordingly suggest some foci for future development in this important technology area.