A Paper Abstract by Benjamin Grosof

Applications of Logicist Knowledge Representation to Enterprise Modelling (July 13 1992)

by Benjamin N. Grosof and Leora Morgenstern

The goal of the enterprise modelling efforts at IBM is to develop methods to model an organizational unit's knowledge and activities. Ultimately, enterprise modelling should support an ongoing, incremental automation process. The problem we address here is the applicability of declarative AI knowledge representation (KR) to enterprise modelling. Does KR have a useful role to play? What is it? What are its limitations?

In an initial case study in the area of purchasing, we find that much of currently non-automated guidelines information can be represented successfully using standard logicist methods. We show how to create very-high-level specifications with well-understood semantics. These specifications are useful as descriptive information in non-executable form; they help identify ambiguities, inconsistencies, and omissions in less formal guidelines information. We also show that these very-high-level specifications are partially executable using standard logicist inference methods such as rule-based programming and/or logic programming. This is useful for development of prototype software and simulations.

Our logicist approach provides a rich language and set of methods for describing and propagating constraints, especially when compared to other less formal approaches such as the Entity-Relationship model. However, we discover that most guidelines information relies heavily on common sense, and raises difficult knowledge representation challenges in temporal, default, decision-theoretic, and multiple-level reasoning that expose the limits of state-of-the-art logicist methods.

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