NACE (Non-Axiomatic Causal Explorer)

NACE is an experiential learning agent designed to overcome the extreme data inefficiency of deep reinforcement learning (DRL). While standard DRL agents require millions of trial-and-error samples to approximate correlations, NACE functions as a causal reasoner: it actively constructs a logic-based model of its environment by observing the direct consequences of its interactions. This approach allows it to master complex grid-world environments with remarkable speed, demonstrating a 1000-fold reduction in data requirements—achieving competence in roughly 1,000 steps where state-of-the-art baselines require over 1,000,000.

Functionally, the agent operates on a cycle of curiosity-driven exploration. NACE generates causal rules from local changes in the environment (e.g., “pushing this block opens that door”) and prioritizes its actions based on an intrinsic reward signal geared toward uncertainty reduction. Rather than merely chasing a game score, it plans paths specifically to reach states where its internal model is incomplete, systematically filling knowledge gaps. Grounded in Non-Axiomatic Logic (NAL), the system is robust to noise: it tracks the evidential weight of every rule (balancing positive vs. negative evidence), allowing it to maintain a stable but plastic worldview that adapts to stochastic environments without the brittleness of traditional symbolic AI.