Leela AI Skill

Manufacturing Intelligence -- from theory to industrial application.

Overview

This skill describes Leela AI's relationship to MOOLLM. Leela develops MOOLLM with an eye toward manufacturing intelligence, using it daily for practical devops, edgebox management, coding, debugging, and design work. The team is exploring how the theoretical foundations of Minsky, Papert, and Drescher might eventually deploy on factory floors.

Leela and Gary Drescher

Leela's foundations lie in Gary Drescher's work at MIT under Marvin Minsky and Seymour Papert. Drescher brought Jean Piaget's developmental psychology into computing: infants learn through sensorimotor experience and build schemas (context → action → result). Henry Minsky was exposed to this as a student; years later he reimplemented Drescher's algorithms and, with Cyrus Shaoul and Milan Minsky, founded Leela AI. The name Leela is Sanskrit for divine play — the play of creation, destruction, and re-creation.

Key points:

• Schema mechanism: Leela builds models of the world using schemas that reason about which actions are possible and what changes when an action is performed. Goals are achieved by chaining schemas (planner finds actions whose results match the goal).
• Self-supervised learning: Leela learns from exploratory actions without labeled examples or explicit reward; it forms and tests hypotheses. In multi-goal grid-world experiments (Kommrusch et al., IWSSL 2020), Leela reached training targets in ~160N² steps vs DQN ~360N^2.7, and does not suffer catastrophic forgetting.
• Neurosymbolic extension: Later work (Symbolic Guidance for Constructivist Learning, Neurosymbolic Learning on Video Data, Society of LLMs) combines the symbolic schema system with neural perception (object/pose detection, cortical columns, multi-LLM instances). Society of LLMs (Kommrusch & Minsky, IWSSL 2024) maps Drescher's schema mechanism onto multi-agent LLMs: curiosity-driven goals, multiple plans, training samples when plans differ and one succeeds, contextual sub-activation (one agent "thinking subconsciously"), and incremental LoRA updates; evaluation target ARC-AGI. Leela Core uses the hybrid for manufacturing video intelligence — causal reasoning and explainability on top of ConvNets.

See: schema-mechanism/, reference/drescher-lineage.yml, reference/publications.yml, reference/society-of-llms.yml.