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Karl Johan Åström

Swedish control theorist who, in 1965 while working at IBM's Nordic Laboratory, published "Optimal Control of Markov Processes with Incomplete State Information" (Journal of Mathematical Analysis and Applications, vol. 10, pp. 174–205) — the paper that gave a full mathematical treatment to the problem of choosing optimal actions when the true state of a system can only be inferred from noisy or incomplete observations.

Matters to this vault as the confirmed next link in a chain it had already started tracking on A.A. Fel'dbaum's page as an unread lead: Fel'dbaum posed the problem of a controller that must simultaneously learn a system's dynamics and act on it (dual control, early 1960s); Åström's 1965 paper is cited as reference [1] in Kaelbling, Littman & Cassandra's 1998 Artificial Intelligence paper, the document that carried the resulting mathematical object — the partially observable Markov decision process — into mainstream AI planning research and, sixty-one years later, into how 2026 papers formalize LLM-agent decision-making.

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