Hyperon AI Algorithms+MetaMo (Motivational Framework)

Responsible: Ruiting Lian, Ben Goertzel

MetaMo models motivation in open-ended intelligent agents as a dynamical system coupling appraisal with decision processes. It shapes PLN control dynamics, regulates exploration–exploitation tradeoffs, and embeds safety constraints within motivational dynamics.

GitHub

</> Example Implementation

Motivational dynamics

Demonstrates MetaMo's motivational framework coupling appraisal with action selection.

; MetaMo goal-motivation system ; Appraisal + decision coupling !(define-goal (goal explore-environment (urgency 0.8) (importance 0.6))) !(metamo-step (appraise current-state goals) (select-action (balance exploration exploitation)))

Technical Deep Dive: MetaMo Full — pseudo-bimonad structure, OpenPsi appraisal comonad + MAGUS decision monad, contractive update dynamics, AGI 2025 papers, and implementation findings.