Hyperon AI Algorithms+ECAN (Economic Attention Networks)

ECAN is the attention-allocation and resource-regulation subsystem of the Hyperon architecture, designed to support cognitive efficiency under conditions of bounded computation and memory. In principle, a Hyperon agent knows everything stored in an Atomspace; in practice, however, attempting to reason over all stored knowledge simultaneously would be computationally intractable. ECAN addresses this problem by continuously regulating which Atoms are actively considered, ensuring that cognitive effort is concentrated on a tractable, context-relevant subset of the knowledge graph at any moment.

This regulation is achieved through two dynamically updated scalar values assigned to each Atom: Short-Term Importance (STI) and Long-Term Importance (LTI). STI captures immediate, context-dependent relevance and is propagated through Hebbian-weighted associative links, enabling attention to shift dynamically as situations, goals, or perceptions change. LTI, by contrast, reflects longer-horizon expected utility — encoding how consistently an Atom has contributed to successful inference, learning, or goal-directed behavior over time. Together, STI and LTI drive a nonlinear, feedback-driven attention dynamic that functions as an internal economy, determining which Atoms remain active, which fade into the background, and which are eventually deprioritized.

At a systems level, ECAN implements an attention protocol that balances short-term responsiveness with long-term coherence. Atoms effectively compete for limited working-memory and processing capacity based on their importance profiles and current context, with those that fail to demonstrate relevance gradually losing activation. By enforcing this dynamic attention economy, ECAN allows Hyperon agents to scale to very large knowledge bases while avoiding exponential blowup in search and inference, preserving real-time deliberative agility under bounded computational resources. In this role, ECAN is a key component of cognitive synergy, enabling systems like PLN and MOSES to operate fluidly by dynamically constraining portions of the knowledge base, addressing one of the key architectural challenges of AGI.