AI Curriculum Based on Historic Board Games

Summary: Introduces Shatranj.AI project, Erasmus+ foundations, partner organizations, digital platforms.

• Introduces the Shatranj.AI project, its Erasmus+ foundations, partner organizations, and digital platforms.
• Project vision, Erasmus KA2 context
• Partner institutions and cultural heritage focus
• Overview of platforms (editor, LMS, code tools)
• Teacher roles and student outcomes
• Curriculum structure overview
• Python/Jupyter introduction

Basic computing concepts and Python/Jupyter installation.
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• Students learn core computing concepts and set up Python/Jupyter.
• CPU, RAM, I/O basics
• Bits, bytes, binary representation
• JupyterLab installation
• First Python notebook execution
• Variables, simple expressions
• Access to Drive folders

Numbers, strings, lists, and essential data handling in Python.
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• Covers Python’s built‑in data types and basic operations.
• Integers, floats, strings, booleans
• Type conversion
• Lists and indexing
• Mutability concepts
• Chess-pieces-as-strings exercises

If/else logic, loops, and control flow fundamentals.
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• Introduces logic, loops, and interactive programs.
• If/elif/else logic
• Boolean operations
• For/while loops
• Break/continue
• Simple input programs

Writing functions, parameters, returns, and variable scope.
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• Teaches modular code with functions.
• Defining functions
• Parameters and returns
• Local/global scope
• Lambdas
• Small functional project (piece-value calculator)

Reading/writing files, handling errors, and using libraries.
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• Working with files and robust code.
• File read/write
• Try/except
• Importing libraries
• Simple testing
• Handling invalid inputs

Classes, objects, and building a simple TicTacToe game.
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• First exposure to OOP.
• Classes and objects
• Attributes and methods
• Game modeling
• TicTacToe implementation
• Debugging OOP code

Board representation and basic piece movement logic.
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• Students start building engine components.
• Board representations
• Piece movement basics
• UTF-8 rendering
• Chess board editor tools
• Printing and moving pieces

Board representation and basic piece movement logic.
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• Students start building engine components.
• Board representations
• Piece movement basics
• UTF-8 rendering
• Chess board editor tools
• Printing and moving pieces

DFS, BFS, UCS, and A* search foundations.
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• Core AI search algorithms for games.
• Search problem structure
• DFS, BFS, UCS
• A* and heuristics
• Minimax introduction
• Pacman/chess examples
• Graph-tracing exercises

Recursion, backtracking, and heuristic solutions.
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• Explores Knight’s Tour with recursion and heuristics.
• Knight graph movement
• Open/closed tours
• Backtracking with DFS
• Warnsdorff heuristic
• Connection to TSP

Recursion, backtracking, and heuristic solutions.
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• Explores Knight’s Tour with recursion and heuristics.
• Knight graph movement
• Open/closed tours
• Backtracking with DFS
• Warnsdorff heuristic
• Connection to TSP

Backtracking and constraint-based problem solving.
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• Constraint satisfaction with backtracking.
• Queen attack logic
• Recursive search
• Optimization techniques
• Historical queen references
• Notebook implementations

Exponential growth and classic logic puzzles.
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• Mathematical puzzles and exponential growth.
• Doubling on chessboard
• Powers of 2
• Magic squares
• Smullyan logic puzzles

Advanced search, pruning, and checkmaet logic.
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• Deep adversarial search and chess endgames.
• Minimax computation
• Alpha-beta pruning
• Opposition, triangulation
• Historical sources (Al-Adli er-Rumi, Alfonso's book)

Historic endgame study and triangulation.
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• Historic chess endgame analysis.
• Al-Suli biography
• Reconstruction of endgame
• Opposition and triangulation
• Corresponding squares theory

Adapting classical engines to play Shatranj.
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Explores how modern chess engines evolved and how open-source engines can be adapted to historical variants.

• Deep Blue’s brute-force hardware search
• Rybka and the rise of evaluation-centric engines
• Stockfish as an open, community-driven engine
• How Stockfish represents pieces, moves, and rules
• Modifying piece movement (ferz, wazir), evaluation, legality rules
• Building Shatranj-compatible search and evaluation

Rook pathfinding and reinforcement learning basics.
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Introduces RL concepts using a simple OpenAI Gym–style gridworld based on rook movement.

• Agent–environment loop
• States, actions, rewards, episodes
• Q-learning and ε-greedy exploration
• Rook navigation on an 8×8 board
• Reward shaping and sparse reward issues
• Visualizing policy improvement step-by-step

Which games can be built using reinforcement learning.
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Overview of small, medium, and complex games that students can use to implement RL algorithms.

• TicTacToe, Connect-4, Gridworld
• Snake, Pacman, Othello/Reversi
• Mini-chess and other board variants
• State space vs. reward density considerations
• Matching games with RL methods (Q-learning, MC, DQN, MCTS)
• Choosing project-friendly RL environments

Training an RL agent to perform classical checkmate.
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Applies RL to a structured chess endgame where the agent must learn the coordinated 2-rook mating pattern.

• Environment design and state encoding
• Sparse rewards and reward shaping for endgames
• Learning piece coordination and zugzwang patterns
• Progressive improvement through self-play
• Visualizing trajectories toward checkmate
• Bridge to AlphaZero-style self-play and value learning

MCTS, neural networks, and building mini-AlphaZero.
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Minimax & Alpha-Beta (bridging classic AI and RL)

• Why pure RL struggles with deep tactics
• Why humans and engines search
• Teach minimax, alpha-beta
• Connect to MCTS logic (search tree with value estimates)
• Very important bridge: AlphaZero = RL + MCTS + neural nets

Introduction to policy/value functions

• States → value
• States → probability distribution over moves
• Explain why tabular methods fail in full chess
• Show that we need function approximation
• Introduce NNEU concept here:

What is NNEU?

– neural network evaluation unit

– a neural net replacing hand-coded evaluation

– outputs value (who is better)

– outputs policy (what moves are likely good)

The students don’t need deep math; only intuition

Deep Q-Learning (DQN) with simple games

• Use Snake or Catch
• Teach replay buffer, target network
• Show why DQN does NOT work for chess (action space too large), preparing AlphaZero.

Policy gradient & actor-critic basics

(Short overview, not too mathematical)

• Policy gradient: learn policy directly
• Actor-critic: policy (actor) + value estimate (critic)
• Prepare students for AlphaZero’s architecture (policy + value output).

Monte Carlo Tree Search (full lesson)

• Expand nodes
• Simulate
• Backup values
• Choose action using visit counts
• Show how policy prior improves MCTS
• Show how MCTS improves the policy network → the AlphaZero training loop emerges

Putting it all together: the AlphaZero algorithm

• Self-play generates games
• MCTS guided by neural net creates improved policy
• Neural net trained on (state, policy_targets, value)
• Iteration of self-play → training → stronger MCTS → stronger games
• Show how AlphaZero solves simple mini-chess (4×4, 5×5)
• Connect back to your 2-rook checkmate lesson

Students now understand:

– how RL can solve small tasks

– why full chess needs powerful NNEUs, MCTS, and self-play

Build a mini AlphaZero for Connect-4 or MiniChess

• Connect-4 board is perfect for classroom AlphaZero demo
• Implement:

– neural net (small CNN or MLP)

– MCTS

– self-play training

Lc0 is a direct open-source implementation inspired by DeepMind's AlphaZero project, and it has far exceeded its success in chess.
https://lczero.org/