Expert System Mini Project (Teaching Version)

Overview

You will build a simple rule-based expert system using forward chaining.

Files Provided

File	Description	Status
engine.py	Core inference engine	Incomplete (you implement key functions)
kb_loader.py	Loads JSON knowledge base	Complete
kb/laptop_rules.json	Sample KB (1 rule only)	Incomplete (you add 9 more)
main.py	Command-line interface	Incomplete
tests/	Folder for test cases	Empty

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Your Tasks

1. Implement the inference logic

   • Complete can_fire(), run(), and conclusions() in engine.py.

2. Expand the knowledge base

   • Add at least 9 more rules to kb/laptop_rules.json.

3. Complete main.py

   • Load rules.
   • Collect user facts.
   • Run inference.
   • Display recommendations and which rules fired.

4. (Optional) Bonus

   • Implement explanations: “Why” and “How” reasoning.
   • Add uncertainty or confidence factors.

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Example Run (after completion)

Is portability important? (y/n): y
Do you need long battery life? (y/n): y
Is your budget high? (y/n): y

=> Recommendation: premium_ultrabook
=> Explanation: derived from rule 'Premium Ultrabook'

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Grading Rubric (100 pts)

Component	Points
Inference engine (engine.py)	40
Rule base completeness	20
Correct reasoning output	20
Code readability & structure	10
Report / explanation clarity	10

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