AIRIS (Autonomous Intelligent Reinforcement Inferred Symbolism)

AIRIS is a causal machine learning system designed to overcome the opacity and data-inefficiency of traditional deep reinforcement learning. Rather than ingesting massive datasets to approximate statistical correlations, AIRIS functions as a causal reasoner. It actively constructs a deterministic model of its environment through direct interaction. This approach yields exceptional transparency, encoding decision logic in explicit, auditable rules rather than inscrutable neural weights.

The system has proven its capabilities in voxel-based environments like Minecraft, where it operates without pre-training. By observing the direct consequences of its actions (e.g., “walking into lava causes damage,” “dirt blocks can be stacked”), AIRIS builds a dynamic knowledge base of causal rewrite rules. It uses these rules to run internal simulations in its dynamically generated world model to plan complex paths and achieve arbitrary goals. Crucially, the system is self-correcting: when a prediction fails, such as falling into an unseen ravine, AIRIS instantly isolates the error and updates its rule set, effectively applying the scientific method to autonomous navigation.

Within the context of Hyperon, AIRIS serves as a useful mechanism for causal learning. It translates raw sensory data from the virtual world into the structured symbolic knowledge of the Atomspace. These grounded atoms become the raw material for higher-level algorithms like PLN and MOSES, which abstract the learned rules into higher level, generalizable strategies. In this role, AIRIS acts as a sensory-motor cortex in certain contexts, providing the foundational physics of reality upon which AGI can reason across diverse environments.