Responsible: Ruiting Lian, Ben Goertzel
GitHub: https://github.com/iCog-Labs-Dev/hyperon-openpsi
Papers:
Description:
MetaMo is a framework for modeling motivation in open-ended intelligent agents, concerned with how goals, priorities, and evaluative signals can be updated over time while preserving coherence, stability, and interpretability. Rather than relying on scalar reward functions or manually engineered drive hierarchies, MetaMo treats motivation itself as a dynamical system, explicitly coupling appraisal processes with decision processes.
MetaMo represents motivational state as a structured interaction between goal intensities and modulatory variables. Appraisal updates modulators such as valence, arousal, and risk sensitivity in response to contextual novelty and task relevance, while decision mechanisms score candidate actions relative to active goals. System stability is enforced via contractive update dynamics that draw motivational state away from pathological extremes.
Within the Hyperon ecosystem, MetaMo serves as the motivational backbone linking inference, learning, and attention allocation. It shapes control dynamics in PLN by biasing search and inference toward contextually appropriate goals, regulates exploration–exploitation tradeoffs, and embeds safety and ethical constraints directly within motivational dynamics rather than as externally imposed rules.
Roadmap: