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Equation 6 · Part 1 · Comparing the Main Approaches to AI Agent Architecture

Symbol P

P=πplan(o0, g),at=πexec(P, ot).P = \pi_{\text{plan}}(o_0,\ g), \qquad a_t = \pi_{\text{exec}}(P,\ o_t).
PP

What this part means

the plan.

Its job in the formula

P is part of the quantity the equation computes from the expression on the right.

Where the article explains it

The plan P is computed against the belief state available at time zero and then held fixed; the executor applies it against a stream of later observations without necessarily invoking the planning policy again.

The passage around this formula

Formally, this replaces the single-loop policy with two functions and one artifact: P=πplan(o0, g),at=πexec(P, ot)P = \pi_{\text{plan}}(o_0,\ g), \qquad a_t = \pi_{\text{exec}}(P,\ o_t). The plan P is computed against the belief state available at time zero and then held fixed; the executor applies it against a stream of later observations without necessarily invoking the planning policy again. That is precisely the assumption a classical open-loop plan makes, and it is a specific instance of the belief-state fragility decision theory already names: a policy computed once from a belief state is only as good as that belief state stays accurate, and nothing in the equation above notices when the two have quietly come apart [ 11 ] .

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Learn the underlying idea

A variable is a named place for a value. Its letter is a local label: x can mean position in one formula and a data point in another.

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Sources cited in the surrounding passage

These citations provide research context; check each source for the exact claim it supports.