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This paper shows how a single mechanism allows knowledge to be constructedlayer by layer directly from an agent's raw sensorimotor stream. Thismechanism, the General Value Function (GVF) or "forecast," captures high-level,abstract knowledge as a set of predictions about existing features andknowledge, based exclusively on the agent's low-level senses and actions.  Thus, forecasts provide a representation for organizing raw sensorimotor datainto useful abstractions over an unlimited number of layers--a long-sought goalof AI and cognitive science.  The heart of this paper is a detailed thought experiment providing aconcrete, step-by-step formal illustration of how an artificial agent can buildtrue, useful, abstract knowledge from its raw sensorimotor experience alone.The knowledge is represented as a set of layered predictions (forecasts) aboutthe agent's observed consequences of its actions. This illustration showstwelve separate layers: the lowest consisting of raw pixels, touch and forcesensors, and a small number of actions; the higher layers increasing inabstraction, eventually resulting in rich knowledge about the agent's world,corresponding roughly to doorways, walls, rooms, and floor plans. I then arguethat this general mechanism may allow the representation of a broad spectrum ofeveryday human knowledge.

Representing Knowledge as Predictions (and State as Knowledge)