A Representation and Learning Mechanism for Mental States

Paul Cohen, Marc Atkin, Tim Dates, and Dawn Gregory

We want to build an agent that plans by imagining sequences of future states. Subjectively, these states seem very rich and detailed. Providing an agent with sufficiently rich knowledge about its world is an impediment to studying this kind of planning, so we have developed mechanisms for an agent to learn about its world. One mechanism learns dependencies between synchronous "snapshots" of the world; the other learns about processes and their relationships.

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