AI Origins · Lesson 11

Robot Repair Clinic

Five broken robots need a diagnosis. Use IF-THEN rules to find each fault — the fewer clues you need, the better your reasoning.

Round 1 / 5 Score: 0 4 inspects left

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Inspect an area (each costs 1 token)

Logic Probe Hover or click a robot subsystem to scan for clues.

Chest: power values · Head: diagnostics · Wheels: motion clues

Clues found so far

AI Rule Engine — live analysis

No diagnosis yet Gather evidence to raise confidence.

Inspect the robot to gather evidence. The AI will update here.

The engine fires IF-THEN rules as each clue arrives.

Your diagnosis

Live Rule Engine Lab

Pick evidence you observe, run the rules, and inspect how the lab combines evidence into best guesses.

This lab simulates how early AI systems reasoned using explicit IF-THEN rules.

You'll encode observations as facts, fire rules, and inspect explanations.

Observe Encode as Facts Fire Rules Produce Explanations

Lab Mode

Easy mode shows core evidence cards and simple explanations.

Evidence You Observe (Known Facts)

Act like a systems engineer: only encode facts you can justify, then set confidence.

Why This Lab Exists

In the 1970s, AI researchers believed intelligence could be written down as rules. This lab lets you experience that approach: transparent reasoning with traceable explanations.

Engine Controls

Conflict Resolution (Policy)

Combine evidence from multiple rules into one confidence score.

Max Forward Passes

Rules in Knowledge Base: 0 (human-authored)
Rules fired: 0
Passes used: 0
Conclusions produced: 0

Rules (Advanced) (Rule Base JSON)

Show Rules (Advanced)

Knowledge Engineer Builder

IF THEN

IF evidence THEN conclusion will appear here.

Logic Flow Laboratory

[ENGINE] Waiting for evidence...

Best Guesses (Confidence) (Conclusions)

Guess	Confidence	Status	Trace

Why It Thinks That (Explanation Trail)

Run the rules and choose "Why?" on a result to see a compact explanation.

Detailed Rule Trail (Advanced)

What This Trains

System Mechanics

Symbolic LogicKnowledge encoded as explicit IF-THEN rules
State MachinesFacts update in deterministic forward passes
Weighted InferenceConfidence factors model uncertainty beyond true/false
ExplainabilityEvery troubleshooting guess can be traced to fired rules

Conceptual Questions

TrustWho authored the rules and where can bias hide?
ConflictWhat should happen when rules disagree?
LimitsWhen does explicit logic stop scaling well?
BridgeHow does this compare to neural-network learning?

Historical Context

This lab represents the Symbolic AI / Expert Systems era (1970s–1980s).

Expert Systems Era

ApproachHuman knowledge formalized as transparent rules
MYCIN-styleCertainty-factor methods showed how rule confidence can combine
StrengthAuditable reasoning path for each recommendation
WeaknessRule authoring burden and brittle domain transfer

AI Timeline Placement

1958
Perceptron era begins neural-symbolic split
1966
ELIZA popularizes scripted language interaction
1970s
Expert systems (including MYCIN-style ideas) scale symbolic AI
2012
Deep learning re-centers data-driven representation learning

Lesson Outputs

Each team should publish a short reflection and demo link at the end of class.

Starter Kit Robotics Troubleshooter live starter workspace Use the built-in forward-chaining lab on this page Extension Add conflict policies that vary by conclusion type Optional challenge for advanced students Navigate Back to STEAM Lessons Return to the full ClassroomOS lesson shelf