Problem-Solving Through Solitaire

Programming is more than writing code — it’s about solving problems systematically. Solitaire (Klondike) is an excellent case study because it requires breaking down a complex game into smaller, solvable challenges.

This lesson explores problem-solving strategies developers use while building Solitaire, from identifying subproblems to testing solutions.

---

Why Solitaire for Problem-Solving?

• Complex but familiar: Rules are well-known, but coding them requires careful logic.
• Multiple interacting parts: Cards, piles, moves, scoring — each introduces unique challenges.
• Step-by-step thinking: You can’t solve everything at once; you must break it down.

---

Core Problem-Solving Strategies Demonstrated

1. Decomposition – Break Down the Problem

Large problems become manageable when split into smaller ones.

Example: Solitaire Decomposition

• Cards → define properties (suit, rank, color, face-up)
• Piles → manage groups of cards with rules
• Moves → validate before executing
• Game flow → deal, play, win conditions

// Start with the smallest piece
class Card {
  constructor(suit, rank, color, value) {
    this.suit = suit;
    this.rank = rank;
    this.color = color;
    this.value = value;
    this.faceUp = false;
  }
}

Key Insight: Instead of asking “How do I build Solitaire?”, ask “How do I represent a single card?” Then solve progressively larger problems.

2. Pattern Recognition – Spot Similarities

Looking for repeating structures helps simplify code and avoid duplication.

Example: Pile Patterns

• All piles hold cards
• All piles need push, pop, and top methods

Differences come from rules (canAccept)

class Pile {
  constructor() {
    this.cards = [];
  }
  top() { return this.cards[this.cards.length - 1]; }
  push(card) { this.cards.push(card); }
}

Problem-Solving Benefit: Once you see patterns, you can create a reusable solution instead of solving the same problem multiple times.

3. Abstraction – Focus on the Big Picture

When rules get complicated, hide details behind simple interfaces.

Example: Move Validation

game.tryMoveCard(cardId, targetPile) {
  const card = this.findCard(cardId);
  if (!card || !card.faceUp) return false;
  if (targetPile.canAccept(card)) {
    targetPile.push(card);
    return true;
  }
  return false;
}

Why it helps: You don’t need to re-check every rule every time — one clean method handles it.

4. Algorithmic Thinking – Design Step-by-Step Processes

Some problems require a logical sequence of actions.

Example: Dealing Cards Shuffle the deck For pile 1, place 1 card (face up) For pile 2, place 2 cards (last face up)

Continue until pile 7

function deal(deck, tableau) {
  for (let i = 0; i < 7; i++) {
    for (let j = i; j < 7; j++) {
      const card = deck.pop();
      tableau[j].push(card);
      if (j === i) card.faceUp = true;
    }
  }
}

Key Idea: Algorithms give structure to otherwise messy tasks.

5. Debugging and Iteration – Test, Fail, Refine

No solution works perfectly the first time. Debugging means testing assumptions, finding mistakes, and improving.

Example Debugging Process

Problem: Kings aren’t stacking correctly in the tableau Hypothesis: Rule in canAccept() is wrong Test: Log values of top() and card Fix: Ensure value === top.value - 1 instead of +1

if (this.isEmpty) return card.rank === 'K';
return (card.color !== top.color && card.value === top.value - 1);

---

Learning Exercises

Exercise 1: Break Down a New Feature

Write out the steps you would need to implement an “Undo Move” feature. (Hint: What information do you need to save each time a move is made?)

Exercise 2: Spot Patterns

Look at the rules for FoundationPile and TableauPile. What similarities exist? How could you reuse code to avoid repetition?

Exercise 3: Debugging Challenge

Suppose cards sometimes disappear when moved between piles.

What would you check first?

How would you test if the bug comes from pop() or push()?

Problem-Solving Benefits Demonstrated

• Clarity – Decomposition helps focus on one piece at a time
• Efficiency – Pattern recognition reduces duplicate work
• Simplicity – Abstraction hides messy details
• Precision – Algorithms provide step-by-step structure
• Resilience – Debugging builds persistence and adaptability

---

Common Mistakes to Avoid

• Trying to solve everything at once → Leads to confusion and messy code
• Copy-pasting instead of recognizing patterns → Hard to maintain later
• Ignoring testing → Bugs multiply if unchecked
• Overcomplicating early → Start simple, add complexity gradually

---

Conclusion

Building Solitaire is a lesson in problem-solving as much as it is about programming. By applying decomposition, pattern recognition, abstraction, algorithmic thinking, and debugging, we learn how to tackle complex challenges systematically.

---