Project 3: The Forward Chaining Engine (The “Production System”)

Build a forward chaining engine that applies rules to facts until no new facts can be derived.

Quick Reference

Attribute Value
Difficulty Level 3: Advanced
Time Estimate 10-16 hours
Language Python
Prerequisites Project 1, rule systems
Key Topics forward chaining, rule firing, conflict resolution

1. Learning Objectives

By completing this project, you will:

  1. Implement rule matching against facts.
  2. Fire rules to derive new facts.
  3. Avoid repeated firing with agendas.
  4. Resolve conflicts between rules.
  5. Trace inference chains.

2. Theoretical Foundation

2.1 Forward Chaining

Forward chaining starts from known facts and applies rules to derive new knowledge.

3. Project Specification

3.1 What You Will Build

A production system that repeatedly applies rules until it reaches a fixed point.

3.2 Functional Requirements

  1. Rule base with conditions and actions.
  2. Matcher to find applicable rules.
  3. Agenda to manage rule firing order.
  4. Conflict resolution strategy.
  5. Trace output of derived facts.

3.3 Non-Functional Requirements

  • Deterministic inference for same input.
  • Readable trace logs.
  • Configurable conflict resolution.

4. Solution Architecture

4.1 Components

Component Responsibility
Matcher Find applicable rules
Agenda Schedule rule firing
Executor Apply actions
Tracer Record inference steps

5. Implementation Guide

5.1 Project Structure

SYMBOLIC_AI_AND_EXPERT_SYSTEMS_MASTERY/P03-forward-chaining/
├── src/
│   ├── rules.py
│   ├── match.py
│   ├── agenda.py
│   └── trace.py

5.2 Implementation Phases

Phase 1: Matcher + agenda (4-6h)

  • Match rules against facts.
  • Checkpoint: applicable rules listed.

Phase 2: Rule firing (4-6h)

  • Apply actions and derive facts.
  • Checkpoint: new facts added.

Phase 3: Conflict resolution (2-4h)

  • Implement priority or recency strategy.
  • Checkpoint: deterministic firing order.

6. Testing Strategy

6.1 Test Categories

Category Purpose Examples
Unit matcher correct rule matches
Integration chaining new facts derived
Regression conflicts deterministic order

6.2 Critical Test Cases

  1. Rule firing stops when no new facts.
  2. Agenda prevents duplicate firing.
  3. Conflict resolution yields stable results.

7. Common Pitfalls & Debugging

Pitfall Symptom Fix
Infinite loops never stops detect no-new-facts
Rule explosion too many rules add priorities
Non-determinism varying results fix conflict strategy

8. Extensions & Challenges

Beginner

  • Add a rule priority system.
  • Add CLI for rules.

Intermediate

  • Add rule salience (weights).
  • Add agenda caching.

Advanced

  • Add RETE algorithm.
  • Add performance profiling.

9. Real-World Connections

  • Production systems power expert systems.
  • Automation rules use forward chaining.

10. Resources

  • Rule engine references
  • RETE algorithm papers

11. Self-Assessment Checklist

  • I can implement forward chaining.
  • I can manage rule conflicts.
  • I can trace rule firing.

12. Submission / Completion Criteria

Minimum Completion:

  • Forward chaining engine

Full Completion:

  • Conflict resolution + tracing

Excellence:

  • RETE-based optimization

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