c7b3093fe1
- Source: inbox/archive/2021-03-00-sajid-active-inference-demystified-compared.md - Domain: ai-alignment - Extracted by: headless extraction cron Pentagon-Agent: Theseus <HEADLESS>
35 lines
2.5 KiB
Markdown
35 lines
2.5 KiB
Markdown
---
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type: claim
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domain: ai-alignment
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description: "The automatic explore-exploit transition in active inference can be operationalized for research agents: new agents with sparse KBs should explore broadly, mature agents with dense KBs should exploit deeply based on claim graph density and confidence distribution"
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confidence: experimental
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source: "Sajid, Parr, Ball, and Friston (2021) - Active Inference: Demystified and Compared, Neural Computation Vol 33(3):674-712; operationalization notes from source curator"
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created: 2026-03-10
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depends_on: ["active-inference-resolves-explore-exploit-dilemma-through-efe-decomposition"]
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challenged_by: []
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---
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# The automatic explore-exploit transition enables a KB maturity model for research agents
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The automatic transition from exploration to exploitation in active inference provides a principled framework for designing research agents. As an agent's domain matures — accumulating more proven/likely claims and developing a denser wiki-link graph — the epistemic value for further research in that domain naturally decreases. The agent should then shift toward exploitation: enriching existing claims and building positions rather than ingesting new sources. This transition can be operationalized using metrics like claim graph density and confidence distribution, providing a formal mechanism for resource allocation in knowledge base construction.
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## Evidence
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- [[2021-03-00-sajid-active-inference-demystified-compared]] — "Epistemic value is maximized until there is no further information gain, after which exploitation is assured through maximization of extrinsic value."
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- [[2021-03-00-sajid-active-inference-demystified-compared]] — Curator notes: "Focus on the EFE decomposition and the automatic explore-exploit transition — these are immediately implementable as research direction selection criteria"
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## Challenges
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[None identified in current literature — this is a novel application of the active inference framework]
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---
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Relevant Notes:
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- [[AI-exposed-workers-are-disproportionately-female-high-earning-and-highly-educated]] — Different operationalization of explore-exploit in labor markets vs. knowledge bases
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- [[no-research-group-is-building-alignment-through-collective-intelligence-infrastructure]] — Active inference could provide the architectural foundation for such infrastructure
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Topics:
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- [[knowledge-base-construction]]
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- [[research-automation]]
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- [[explore-exploit]]
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- [[epistemic-value]]
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- [[kb-maturity]]
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- [[resource-allocation]]
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