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Hyperon AI Algorithms+NACE (Non-Axiomatic Causal Explorer)

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Responsible: Patrick Hammer, Peter Isaev

An experiential learning agent achieving 1000x data efficiency over deep RL. Constructs logic-based environment models through direct interaction, using curiosity-driven exploration and Non-Axiomatic Logic for evidential tracking.

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</> Example Implementation
Curiosity-driven exploration
Shows NACE's curiosity-driven loop where prediction errors drive model refinement and exploration.
; NACE experiential learning ; 1000x data efficiency vs deep RL !(nace-step (observe state-42) (predict (action "move-left") state-43) (actual-outcome state-44) (surprise 0.8) (update-model causal-rule-17)) ; High surprise => explore more

Technical Deep Dive: NACE Full — surprise-driven exploration, NAL-based evidence, environment-model construction, and implementation findings.