CheckersAI

Intelligent Board-Game Strategy

Overview

From September 2022 to December 2022, I developed CheckersAI, an AI-driven Checkers agent designed to showcase intelligent decision-making in board games. Unlike a typical graphical mobile app, this was a full-fledged Python project focusing on algorithmic depth, strategic heuristics, and real-time move adaptations. I took a hands-on approach—coding, debugging, and testing—to ensure the AI could dynamically handle complex board states and demonstrate high-level play.

Throughout this project, I leveraged my app development mindset to create user-friendly modules (e.g., for searching, evaluating moves, and simulating board states) while simultaneously employing advanced AI techniques like alpha-beta pruning and heuristic-based evaluation. The result was an agent capable of making efficient and adaptable moves, effectively outperforming many baseline strategies.


Core Highlights

  1. Full-Cycle AI Development

    • Organized the project from requirements gathering (e.g., rules of Checkers, desired difficulty) to final coding and testing.
    • Implemented in Python, ensuring modular code structure and maintainability.

  2. Alpha-Beta Search with Heuristics

    • Designed an evaluation function to reward aggressive captures and positional advantages while penalizing moves leaving pieces vulnerable.
    • Integrated alpha-beta pruning to reduce computational overhead, enabling deeper lookahead within feasible time.

  3. Dynamic Board Adaptation

    • Developed a custom Board class for real-time state updates, undo/redo functionality, and robust move validation.
    • Handled edge cases (king promotions, multi-jump scenarios) and user-defined parameters (variable board dimensions).

  4. Debugging & Problem-Solving

    • Overcame issues with incorrectly accessed board functions, eventually refining the usage of helper methods (e.g., checking if a piece is king).
    • Fine-tuned numeric weightings (for example, awarding +7 for successful captures or penalizing -7 when pieces got jumped) to strike a balance between aggression and safety.


Technical Approach

1. Search Algorithm (Alpha-Beta Pruning)

  • Depth-Limited Search: Set a default search depth (e.g., 3 or 4 ply) to manage compute time.
  • Pruning Efficiency: Employed alpha-beta cuts to prune non-promising branches early, enabling the AI to explore more critical positions in limited time.
# Example snippet of alpha-beta logic (simplified)
best_move = self.alpha_beta_search(self.board, self.depth_search, -100000, 100000, 0)
self.board.make_move(Move(best_move), self.color)

2. Heuristic-Based Evaluation

  • Positive Scores: Jump captures, edge-protecting moves, king promotions.
  • Negative Scores: Exposing pieces to easy captures, repetitive or stagnant moves, vacating safe rows too soon.
  • Adaptive Weight Tuning: Incrementally tested and adjusted these values (+7 for jumping, etc.) to achieve an optimal balance of offense and defense.
# Example excerpt from 'evaluate' function
if len(eval_board.saved_move[-2][1]) != 0:
    jumpLen = len(eval_board.saved_move[-2][1])
    point += jumpLen * 7  # Reward capturing opponent pieces
...
if eval_board.board[last_move[-1][0]][last_move[-1][1]].is_king:
    point += 3  # Additional reward for getting a king

3. Board & Move Management

  • Board Representation: Created a 2D list of Checker objects, each storing color, location, and a boolean is_king.
  • Move Class: Encapsulated sequences of jumps or slides, including multi-step captures.
  • Undo Feature: Implemented a saved_move stack to revert the board to previous states, simplifying debugging.


My Additional Responsibilities

  • Requirements & Rule Analysis: Learned and adapted official Checkers rules, verifying correct move patterns (e.g., forced jumps, multi-hops).
  • Testing & Tuning: Ran numerous trials against baseline AIs (e.g., Random or Poor AI) to measure improvements, adjusting scoring parameters.
  • Report & Documentation: Authored the final AI report, outlining the approach to alpha-beta, heuristics, encountered issues, and enhancements.


Project Outcomes

  • Intelligent Gameplay: The agent effectively balanced offense and defense, often securing wins against simpler AI opponents.
  • Modular, Extensible Code: Thanks to clear data structures (Board, Checker, Move), the logic can be extended to variations like larger boards or custom scoring.
  • Insights into Tuning: Fine-tuning numeric weights taught me how delicate heuristic adjustments can significantly alter AI behavior—mirroring real-world complexities in software parameter optimization.
  • Advanced Debugging Skills: Tracked down subtle function usage errors and improved my ability to refactor code swiftly under changing requirements.


Personal Growth & Reflection

  • Algorithmic Rigor: Gained deeper insight into search algorithms, heuristics design, and the interplay of time complexity vs. depth in game AI.
  • Adaptability: Strengthened my skill in pivoting quickly—an essential trait in app development—when faced with ambiguous or evolving specs.
  • Communication & Documentation: Learned to present complex AI concepts in accessible terms, ensuring thorough knowledge transfer to teammates or stakeholders.


Final Thoughts

Through CheckersAI, I honed my app developer skill set in a less traditional context—building an AI agent rather than a standard mobile interface. The experience underscores my passion for tackling challenging problems with robust code, algorithmic insights, and strategic creativity. This project reflects my readiness to develop high-performance solutions that blend intuitive design, logical architecture, and optimized algorithms—assets I’m eager to contribute to any forward-thinking engineering team.