Approximate Reasoning about Complex Objects in Distributed Systems: Rough Mereological Formalization ?

We propose an approach to approximate reasoning by systems of intelligent agents based on the paradigm of rough mereology. In this approach, the knowledge of each agent is formalized as an information system (a data table) from which similarity measures on objects manipulated by this agent are inferred. These similarity measures are based on rough mereological inclusions which formally render degrees for one object to be a part of another. Each agent constructs in this way its own rough mereological logic in which it is possible to express approximate statements of the type: an object x satisses a predicate in degree r: The agents communicate by means of mereological functors (connectives among distinct rough mereological logics) propagating similarity measures from simpler to more complex agents; establishing these connectives is the main goal of negotiations among agents. The presented model of approximate reasoning entails such models of approximate reasoning like fuzzy controllers, neural networks etc. Our approach may be termed analytic, in the sense that all basic constructs are inferred from data.