We present Score, a rule engine designed and implemented for the Scone knowledge base system. Scone is a knowledge base system designed for storing and manipulating rich representations of general knowledge in symbolic form. It represents knowledge in the form of nodes and links in a network structure, and it can perform basic inference about the relationships between different elements efficciently. On its own, Scone acts as a sort of "smart memory" that can interface with other software systems. One area of improvement for Scone is how useful it can be in supplying knowledge to an intelligent agent that can use the knowledge to perform actions and update the knowledge base with its observations.

We augment the Scone system with a production rule engine that automatically performs simple inference based on existing and newly-added structures in Scone's knowledge base, potentially improving the capabilities of any planning systems built on top of Scone. Production rule systems consist of "if-then" production rules that try to match their predicates to existing knowledge and fire their actions when their predicates are satisfied. We propose two kinds of production rules, if-added and if-needed rules, that differ in how they are checked and fired to cover multiple use cases. We then implement methods to efficiently check and fire these rules in a large knowledge base. The new rule engine is not meant to be a complex stand alone planner, so we discuss how it fits into the context of Scone and future work on planning systems.

42 pages

Thesis Committee:
Scott E. Fahlman (Chair)
Alessandro Oltramari (Bosch Research & Technology Center)

Srinivasan Seshan, Head, Computer Science Department
Martial Hebert, Dean, School of Computer Science

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