Comparing formal theories of context in artificial intelligence

The problem of context has a long tradition in different areas of artificial intelligence (AI). However, formalizing context has been widely discussed only since the late 80s, when J. McCarthy argued that formalizing context was a crucial step toward the solution of the problem of generality. Since then, two main formalizations have been proposed in AI: Propositional Logic of Context (PLC) and Local Models Semantics/MultiContext Systems (LMS/MCS). In this paper, we propose the first in depth comparison between these two formalizations, both from a technical and a conceptual point of view. The main technical result of this paper is the formal proof of the following facts: (i) PLC can be embedded into a particular class of MCS, called MPLC; (ii) MCS/LMS cannot be embedded in PLC using only lifting axioms to encode bridge rules, and (iii) under some important restrictions (including the hypothesis that each context has finite and homogeneous propositional languages), MCS/LMS can be embedded in PLC with generic axioms. The last part of the paper contains a comparison of the epistemological adequacy of PLC and MCS/LMS for the representation of the most important issues about contexts.