Reid G. Smith

Design of knowledge-based systems with a knowledge-based assistant

The authors propose a model for an intelligent assistant to aid in building knowledge-based systems (KBSs) and discuss a preliminary implementation. The assistant participates in KBS construction, including acquisition of an initial model of a problem domain, acquisition of control and task-specific inference knowledge, testing and validation, and long-term maintenance of encoded knowledge. The authors present a hypothetical scenario in which the assistant and a KBS designer cooperate to create an initial domain model and then discuss five categories of knowledge the assistant requires to offer such help. They discuss two software technologies on which the assistant is based: an object-oriented programming language, and a user-interface framework. >

A modular tool kit for knowledge management

We describe an integrated programming environment for develop ing knowledge-based systems. The environment contains a variety of general-purpose tools: a rule interpretation system, a semantic integrity manager, a task representation system and a file manager. Although the tools have different origins (eg. rule based systems, database management, process control), an obiect-oriented foundation lends modularity and consistency to the tool kit.

A conceptual clustering program for rule generation

We present an Interesting Situation Generator (ISG) that assists in the synthesis of interpretation rules from basic domain knowledge. The ISG is a hierarchical clustering program that discovers equivalence classes of situations (e.g. types of geological formations) that give rise to qualitatively distinct manifestations (e.g. different patterns of geophysical measurements corresponding to types of geological formations). The equivalence classes can be used by a rule generator to construct an initial set of interpretation rules of the form manifestation ⇒ situation.

Learning apprentice systems research at Schlumberger

We are developing a Learning Apprentice System (LAS) that partially automates the initial construction of a rule-based performance program from first principle domain knowledge and assists in refining the performance program’s rules during routine use. A simple implementation has been constructed that demonstrates the feasibility of building such a system.