The problem of context has a long tradition in different areas of artificial intelligence (AI). However, the issue of formalizing context has become a widely discussed issue only 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), by Buvac and Mason; and Local Models Semantics/MultiContext Systems MC/LMS, by Ghidini and Giunchiglia / Giunchiglia and Serafini. 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 result of this paper is a formal proof that PLC is less general than MC/LMS, as it can be embedded into a MultiContext System (called MPLC). We argue that this in mainly due to the weak form of locality formalized in PLC. Since in many applications the stronger form of locality seems to be needed, we conclude that MC/PLC is a more adequate formalizations of context, and that it better match the intuitive desiderata that stay behind the need of formalizing context in AI

Comparing Formal theories of Context in AI

Serafini, Luciano
2004-01-01

Abstract

The problem of context has a long tradition in different areas of artificial intelligence (AI). However, the issue of formalizing context has become a widely discussed issue only 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), by Buvac and Mason; and Local Models Semantics/MultiContext Systems MC/LMS, by Ghidini and Giunchiglia / Giunchiglia and Serafini. 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 result of this paper is a formal proof that PLC is less general than MC/LMS, as it can be embedded into a MultiContext System (called MPLC). We argue that this in mainly due to the weak form of locality formalized in PLC. Since in many applications the stronger form of locality seems to be needed, we conclude that MC/PLC is a more adequate formalizations of context, and that it better match the intuitive desiderata that stay behind the need of formalizing context in AI
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11582/530
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