Users are always right…even when they are wrong: Making Knowledge Representation Useful and Usable

摘要

Users' problems in developing applications rarely factor easily so as match the solutions provided by the Knowledge Representation community. If KR is to be applied successfully, the gap between users' "problem space" and the KR "solution space" must be addressed. Two approaches are suggested: "intermediate representations" at a higher level of abstraction than current KR languages and a set of application oriented questions to expose how different systems address common user questions. The results are likely to be hybrid systems including heuristics and 'non-standard' reasoning.