A Theory of Formalisms for Representing Knowledge
This provides a theoretical foundation to partially resolve debates like declarative vs. procedural and symbolic vs. connectionist AI, offering insights for researchers in AI and knowledge representation.
The paper tackles the long-standing dispute over the best formalism for representing knowledge in AI by proposing a general framework to capture various formalisms, proving that all universal formalisms are recursively isomorphic and that intertranslatable formalisms with padding property are also isomorphic, implying they are essentially the same up to offline compilation.
There has been a longstanding dispute over which formalism is the best for representing knowledge in AI. The well-known "declarative vs. procedural controversy" is concerned with the choice of utilizing declarations or procedures as the primary mode of knowledge representation. The ongoing debate between symbolic AI and connectionist AI also revolves around the question of whether knowledge should be represented implicitly (e.g., as parametric knowledge in deep learning and large language models) or explicitly (e.g., as logical theories in traditional knowledge representation and reasoning). To address these issues, we propose a general framework to capture various knowledge representation formalisms in which we are interested. Within the framework, we find a family of universal knowledge representation formalisms, and prove that all universal formalisms are recursively isomorphic. Moreover, we show that all pairwise intertranslatable formalisms that admit the padding property are also recursively isomorphic. These imply that, up to an offline compilation, all universal (or natural and equally expressive) representation formalisms are in fact the same, which thus provides a partial answer to the aforementioned dispute.