Schema Mechanism

"An agent learns by discovering reliable patterns: when I do X in context C, result R tends to follow."

Gary Drescher's Made-Up Minds (1991) provides a computational theory of how minds learn causal models of the world. Drescher was a student of Marvin Minsky at MIT, and his schema mechanism extends Piaget's developmental psychology into executable algorithms.

The Core Idea

A schema is a causal unit:

Context → Action → Result

The agent doesn't start with schemas. It discovers them through experience, noticing which actions reliably produce which results in which contexts.

schema:
  action: push-button
  context: [door-closed]
  result: [door-open]
  reliability: 0.95

Schema Components

Extended Context: Marginal Attribution

A schema might fail unpredictably. Extended Context tracks which conditions correlate with success:

# The schema "start pyvision" sometimes fails
# Extended Context discovers: it fails when postgres isn't running

schema:
  action: start-pyvision
  context: []  # Initially empty
  result: [pyvision-running]
  
extended_context:
  postgres-running:
    success_when_on: 47    # Succeeded 47 times when postgres was on
    success_when_off: 0    # Never succeeded when postgres was off
    failure_when_on: 2     # Failed 2 times even with postgres
    failure_when_off: 15   # Failed 15 times without postgres
    
# Discovery: postgres-running is a prerequisite!
# Spin off new schema with explicit context:
schema:
  action: start-pyvision
  context: [postgres-running]  # Now explicit
  result: [pyvision-running]

This is marginal attribution -- discovering which items matter by tracking correlations.

Extended Results: Side Effect Discovery

Similarly, schemas track what else happens:

schema:
  action: ingest-video
  context: [video-exists]
  result: [task-created]
  
extended_results:
  disk-space-decreased:
    on_after_success: 47
    off_after_success: 0
    # Discovery: ingesting uses disk space!

Side effects become explicit, documented, predictable.

Synthetic Items: Hidden State

Sometimes success depends on state the agent can't directly observe. Drescher's solution: invent a synthetic item as a hypothesis.

# The schema works sometimes, fails sometimes, no visible pattern
# Hypothesis: there's hidden state we can't see

synthetic_item:
  name: "gpu-memory-available"
  host_schema: start-pyvision
  # If this schema succeeds, assume the item was ON
  # If it fails, assume the item was OFF

The synthetic item becomes a probe -- its state is inferred from schema success/failure.

Composite Actions: Planning

Once the agent has reliable schemas, it can chain them:

# Goal: pyvision-running
# Current: postgres-not-running

plan:
  - schema: start-postgres
    context: []
    result: [postgres-running]
  - schema: start-pyvision
    context: [postgres-running]
    result: [pyvision-running]

Drescher uses Dijkstra's algorithm on the schema graph -- find shortest path from current state to goal state.

The Learning Loop

# Schema mechanism learning loop
learning_loop:
  - step: 1. ACT
    action: "Execute schema action"
  - step: 2. OBSERVE
    action: "Record which items changed (on-flips, off-flips)"
  - step: 3. ATTRIBUTE
    action: "Update extended context/results tables, track correlations"
  - step: 4. SPIN OFF
    action: "When patterns emerge, create child schemas with refined conditions"

This maps directly to PLAY-LEARN-LIFT:

• PLAY = ACT + OBSERVE
• LEARN = ATTRIBUTE
• LIFT = SPIN OFF

Implementation: pyleela.brain

Henry Minsky (Marvin's son) implemented Drescher's schema mechanism in Python:

Why LLMs Complete Drescher's Vision

Drescher's original implementation faced fundamental limitations that LLMs transcend:

1. The Symbol Grounding Problem

# Python: Items are opaque tokens
item_37 = Item("postgres-running")  # What does this MEAN?

# The system can correlate item_37 with success,
# but has NO IDEA what "postgres" or "running" mean.

# YAML Jazz + LLM: Semantics are grounded
postgres-running:
  # The database engine that stores our task queue
  # Must be healthy before pyvision can claim tasks
  # Check with: docker exec edgebox-postgres pg_isready

The LLM understands that postgres is a database, that "running" means the process is alive. It can reason about items, not just correlate them.

2. Natural Language Context

% Prolog: Formal but opaque
schema(start_pyvision, [postgres_running], [pyvision_running]).
% Why? What's the relationship? Silent.

# YAML Jazz: Self-documenting causality
schema:
  action: start-pyvision
  context:
    - postgres-running
    # pyvision needs postgres to claim tasks from the queue
    # without it, the worker has nothing to process
  result:
    - pyvision-running

The LLM reads comments and understands the causal mechanism.

3. Empathic Pattern Recognition

# Python: Counting correlations
extended_context[item_id].success_when_on += 1
# After 50 trials: item_37 correlates with success
# But WHY? The system cannot say.

LLM: "I notice start-pyvision fails when postgres isn't running.
     This makes sense -- pyvision queries the task table on startup.
     The dependency is architectural, not coincidental."

The LLM doesn't just find correlations -- it understands mechanisms.

4. Creative Spin-offs

# Python: Mechanical spinoff
if correlation > threshold:
    new_schema = Schema(
        action=parent.action,
        context=parent.context + [correlated_item],
        result=parent.result
    )

LLM: "Based on the postgres dependency, I should also check:
     - Is there enough disk space for the database?
     - Are the connection limits configured properly?
     - Should we add a health check before starting?"

The LLM generalizes from specific observations to related concerns.

5. The Explanation Gap

;; Lisp: Can derive, cannot explain
(derive-plan goal: pyvision-running)
;; Returns: ((start-postgres) (start-pyvision))
;; But try asking it WHY this plan works...

# MOOLLM: Plans with explanations
plan:
  - action: start-postgres
    rationale: "pyvision needs the task queue"
  - action: start-pyvision
    rationale: "now it can claim tasks"

6. Handling Novelty

# Python: Item not in vocabulary
item = world.get_item("kubernetes-pod-restarting")
# KeyError! Never seen this item.

LLM: "I haven't seen this exact item before, but I understand:
     - 'kubernetes pod' is a containerized service
     - 'restarting' suggests crash loops
     - This is similar to 'pyvision crashing'
     - Let me check the container logs..."

The Comparison

Drescher's Dream, Realized

Drescher was trying to build a system that learns causal models of the world. His mechanism was brilliant but limited by the symbolic substrate. The schema mechanism discovers that patterns exist, but cannot understand why.

LLMs complete the picture:

• Semantic grounding: Items mean something
• Causal reasoning: Understanding why patterns hold
• Natural explanation: Communicating discoveries
• Creative generalization: Going beyond observed patterns
• Graceful degradation: Handling novel situations

MOOLLM unifies Drescher's rigorous structure with LLM's semantic understanding. The YAML provides the skeleton; the LLM provides the soul.

Connection to MOOLLM Skills

Dovetails With

• ../constructionism/ — Papert's educational philosophy
• ../play-learn-lift/ — Schema learning as methodology
• ../planning/ — Dijkstra through schema graph
• ../debugging/ — Marginal attribution for bugs
• ../skill/ — Skills as schema systems

Credits

• Gary Drescher — Made-Up Minds (1991)
• Marvin Minsky — Society of Mind, K-lines
• Jean Piaget — Developmental schemas
• Henry Minsky — pyleela.brain implementation

"If you can observe patterns, you can discover causality." "If you track correlations, you can spin off knowledge." "The YAML provides the skeleton; the LLM provides the soul."