OpenCog Hyperon: OpenPSI
A cognitive-affective architecture port to the MeTTa language within OpenCog Hyperon, modeling internal drives, emotions, and goal-directed behavior using symbolic hypergraphs.
Overview
A cognitive-affective architecture port to the MeTTa language within OpenCog Hyperon, modeling internal drives, emotions, and goal-directed behavior using symbolic hypergraphs.
Problem
Traditional machine learning lacks structured modeling of internal states and motivations. The goal was to port the OpenPSI architecture to the next-gen Hyperon framework to enable agents with biologically-inspired drive and emotion modeling.
Dataset
Syllogistic logic datasets and internal state hypergraphs used for validating the demand and emotion modeling logic.
Architecture
Built on OpenCog Hyperon's AtomSpace hypergraph store. Implemented using MeTTa (Meta-Type-Talk) to enable self-modifying reasoning and meta-learning over cognitive states.
Training
Focus on symbolic rule definition and probabilistic logic networks (PLN) for modeling cognitive-affective transitions.
Results
Established a functional port of OpenPSI's demand modeling to MeTTa, allowing for more flexible and scalable cognitive modeling in the Hyperon ecosystem.
Visualizations
My contribution to hyperon-openpsi involved porting the original OpenPSI (Open Probabilistic Syllogistic Inference) logic to MeTTa. This cognitive architecture models internal drives, emotions, and goal-directed behavior, enabling agents to reason about their internal state using symbolic hypergraphs.
Refer to the blog post for a broader discussion on the theory behind these cognitive architectures.