Project 1: The Pattern Matching Fact Base (The “Database” of AI)

Build a fact base with pattern matching rules to store and retrieve symbolic knowledge.

Quick Reference

Attribute Value
Difficulty Level 2: Intermediate
Time Estimate 8-12 hours
Language Python
Prerequisites Basic data structures
Key Topics facts, pattern matching, unification

1. Learning Objectives

By completing this project, you will:

  1. Represent facts as structured tuples.
  2. Implement pattern matching with variables.
  3. Support query and retrieval operations.
  4. Build a rule base for inference.
  5. Measure query performance on larger fact sets.

2. Theoretical Foundation

2.1 Fact Bases

Symbolic systems store knowledge as facts and rules. Pattern matching is how queries map to stored facts.

3. Project Specification

3.1 What You Will Build

A fact base that supports inserting facts, querying by pattern, and returning bindings.

3.2 Functional Requirements

  1. Fact store with insert and lookup.
  2. Pattern matcher with variables.
  3. Query engine returning bindings.
  4. Rule support for derived facts.
  5. Benchmarking for query speed.

3.3 Non-Functional Requirements

  • Deterministic outputs for queries.
  • Clear representation of variable bindings.
  • Configurable indexing for scale.

4. Solution Architecture

4.1 Components

Component Responsibility
Fact Store Store tuples
Matcher Unify patterns with facts
Query Engine Execute queries
Index Speed up lookups

5. Implementation Guide

5.1 Project Structure

SYMBOLIC_AI_AND_EXPERT_SYSTEMS_MASTERY/P01-fact-base/
├── src/
│   ├── facts.py
│   ├── match.py
│   ├── query.py
│   └── benchmark.py

5.2 Implementation Phases

Phase 1: Fact store (3-4h)

  • Add insert and lookup.
  • Checkpoint: facts retrievable.

Phase 2: Pattern matcher (3-5h)

  • Implement variable bindings.
  • Checkpoint: patterns return bindings.

Phase 3: Query + benchmark (2-3h)

  • Add query API and benchmark.
  • Checkpoint: report shows query latency.

6. Testing Strategy

6.1 Test Categories

Category Purpose Examples
Unit matcher binding correctness
Integration query correct results
Regression benchmark stable timings

6.2 Critical Test Cases

  1. Variable binding returns correct values.
  2. Query with multiple variables returns all matches.
  3. Indexed queries outperform brute-force.

7. Common Pitfalls & Debugging

Pitfall Symptom Fix
Variable collisions wrong bindings rename variables per query
Slow queries high latency add indexes
Ambiguous bindings inconsistent output standardize binding order

8. Extensions & Challenges

Beginner

  • Add deletion support.
  • Add simple CLI for queries.

Intermediate

  • Add indexing by predicate.
  • Add rule-based fact derivation.

Advanced

  • Add persistence and reload.
  • Add incremental indexing.

9. Real-World Connections

  • Expert systems rely on fact bases.
  • Knowledge graphs use pattern matching for queries.

10. Resources

  • Unification algorithms references
  • Prolog fundamentals

11. Self-Assessment Checklist

  • I can implement pattern matching with variables.
  • I can query facts and return bindings.
  • I can benchmark query performance.

12. Submission / Completion Criteria

Minimum Completion:

  • Fact store + pattern matching

Full Completion:

  • Query engine + benchmarks

Excellence:

  • Indexing and persistence

This guide was generated from project_based_ideas/AI_AGENTS_LLM_RAG/SYMBOLIC_AI_AND_EXPERT_SYSTEMS_MASTERY.md.