๐Ÿ‘‘

Kingdom Advisor

An Interactive Expert System for Royal Decision Making

Intelligent Decision Support
โš”๏ธ
Scenario
0 / 0
๐Ÿ†
Score
0
โœ…
Correct
0
๐Ÿ”ฅ
Streak
0
๐Ÿฐ

Welcome, Royal Advisor

You serve as the trusted advisor to the monarch. Your kingdom faces threats, opportunities, and dilemmas. Use your wisdom to guide the ruler through challenging decisions.

This game demonstrates Expert System concepts from Intelligent Decision Support Systems.

Knowledge Base Inference Engine Forward Chaining Explanation Facility
๐Ÿ“š Knowledge Base Rules

The knowledge base contains IF-THEN rules that the inference engine evaluates using forward chaining. Each rule maps conditions (facts) to recommended actions.

๐Ÿ“‹ Inference Engine Log

Real-time trace of the inference engine's reasoning process. Shows which rules were evaluated, fired, and what conclusions were reached.

๐Ÿ”

No inference steps yet. Start playing to see the engine in action!

โ„น๏ธ Expert System Components
๐Ÿ“š

Knowledge Base (KB)

A structured repository of IF-THEN rules encoding domain expertise. In this game, rules map kingdom conditions (threats, resources, morale) to recommended royal decisions.

โš™๏ธ

Inference Engine (IE)

The core reasoning mechanism that processes facts against the knowledge base. It determines which rules apply to the current situation and derives conclusions.

๐Ÿ”—

Forward Chaining

A data-driven reasoning strategy. Starting from known facts, the engine matches conditions in rules, fires applicable rules, and generates new facts until a conclusion is reached.

๐Ÿ’ฌ

Explanation Facility

Provides transparency into the reasoning process, showing WHY a particular recommendation was made. It traces the chain of rules and facts that led to the conclusion.

๐Ÿ“

Working Memory

A temporary store of currently known facts. As the inference engine fires rules, new facts are added to working memory, potentially triggering additional rules.

๐ŸŽฏ

Conflict Resolution

When multiple rules match, a strategy determines which to fire first. Common approaches include specificity (most conditions), recency, and priority-based resolution.

๐ŸŽ“ Learning Objectives
1๏ธโƒฃ

Understand Rule-Based Systems

Learn how domain knowledge is encoded as production rules and how they work together to support complex decision-making.

2๏ธโƒฃ

Master Forward Chaining

Observe how a data-driven approach builds conclusions step-by-step from initial facts through rule activation.

3๏ธโƒฃ

Appreciate Transparency

Understand why explainable AI matters โ€” see exactly how the system reaches its recommendations through the explanation facility.