Toward an Abstract Machine Architecture for Intelligence

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Our research defines a uniform framework for analyzing a representative sampling of existing architectures and frameworks for knowledge-intensive intelligent agents. We use this framework to build abstract definitions of representational and functional components that are common across architectures. Defining these abstract components essentially allows us to describe a target abstract machine architecture for knowledge-intensive intelligent systems. This abstract layer should provide a useful tool for comparing and evaluating architectures, as well as for building higher-level languages and tools that reduce the expense of developing knowledge-intensive agents.