NACE (Non-Axiomatic Causal Explorer)
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.
</> 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.