We identify practical software design requirements for rule-based intelligence in the next generation of commercial information agents. Besides basic inferencing, these include embeddability, reusability, user-friendly authoring of rules, communicability of rules, flexibility especially of inferencing control strategy and performance, and extensibility of representation and reasoning. We develop an architecture that fulfills these requirements to a substantial degree: RAISE (Reusable Agent Intelligence Software Environment). RAISE provides building blocks for embeddable agent smarts. It is founded upon a declarative representation and clean semantics, equipped with a sample yet powerful appraoch to procedural attachments. This results in highly pluggable components for inferenciing, authoring, and communication embodied in a fine-grained object-oriented class library. We have found RAISe to enable high reusability of both code and knowledge while embedding rule-based intelligence enhancements in three prototyped infromation agent applications: personal messaging, newgroups filtering and handling for customer service support (the Globenet system), and collaborative news service in Lotus Notes.
By: Benjamin N. Grosof, David W. Levine, Hoi Y. Chang, Colin J. Parris and Joshua S. Auerbach
Published in: RC20305 in 1995
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