The Myth of Modularity in Rule-Based Systems
This work critiques a foundational assumption in AI knowledge representation, highlighting limitations for developers of rule-based systems in uncertain domains.
The paper examines modularity in rule-based systems, distinguishing syntactic and semantic modularity, and argues that while syntactic modularity holds under certainty, semantic modularity often fails in plausible reasoning, as illustrated with the MYCIN certainty factor model, showing that it imposes strong restrictions rarely valid in practice.
In this paper, we examine the concept of modularity, an often cited advantage of the ruled-based representation methodology. We argue that the notion of modularity consists of two distinct concepts which we call syntactic modularity and semantic modularity. We argue that when reasoning under certainty, it is reasonable to regard the rule-based approach as both syntactically and semantically modular. However, we argue that in the case of plausible reasoning, rules are syntactically modular but are rarely semantically modular. To illustrate this point, we examine a particular approach for managing uncertainty in rule-based systems called the MYCIN certainty factor model. We formally define the concept of semantic modularity with respect to the certainty factor model and discuss logical consequences of the definition. We show that the assumption of semantic modularity imposes strong restrictions on rules in a knowledge base. We argue that such restrictions are rarely valid in practical applications. Finally, we suggest how the concept of semantic modularity can be relaxed in a manner that makes it appropriate for plausible reasoning.