typical-cyber/breakthrough-data

Breakthrough MCVS - Zone Guided AI

Advanced Monte-Carlo Value Search (MCVS) engine for the game Breakthrough (8x8), powered by a displacement-based ABC Model and Weighted Adjacency Matrices with Hilbert-ordered Zone Guidance.

This implementation adapts the zone-guided MCVS framework to the simple but illustrative game Breakthrough, keeping the same neural architectures and zone-database design used by the chess reference implementation.

Core Idea

The engine uses:

• Displacement-based ABC Model with homogeneous coordinates to represent piece displacements succinctly
• Dynamic Weighted Adjacency Matrices W = A ⊙ S ⊙ F representing spatial, adjacency and feature similarity
• Hilbert curve ordering for efficient neighborhood (zone) lookup and compression
• A learned Zone Database that stores winning/losing/drawing position-pattern matrices and provides a k-NN based zone score
• Zone Guidance integrated into PUCT (λ-PUCT) to bias MCTS toward favorable zones

The Breakthrough variant uses an internal 8×8 numpy board with lightweight move tuples (fr, fc, tr, tc). Policy outputs are flattened 4096-length move logits (from-square * 64 + to-square), and the value net predicts game outcome in [-1,1].

Files Overview

File	Purpose
breakthrough_mcvs.py	Full MCVS implementation for Breakthrough: game logic, ABC/WeightedMatrix classes, Policy/Value CNNs, Zone DB, MCVS & UCT searchers, self-play and training loop.
breakthrough_zone_db.npz	Zone database file: stores Hilbert-ordered matrices for winning, losing, and draw zones used by zone guidance. Created/updated by breakthrough_mcvs.py.

Notes

• The policy network maps a 1×64×64 weighted matrix tensor to a 4096-dimensional logits vector for flat move indexing.
• The zone DB uses k-NN similarity (L1 normalized) across Hilbert-ordered matrices and returns a zone score Z ∈ [-1, 1].
• breakthrough_mcvs.py includes a training loop that performs self-play data generation, incremental training, checkpointing (breakthrough_checkpoint.pt) and periodic MCVS vs UCT evaluation.

For implementation details, inspect breakthrough_mcvs.py. If you want a shorter quick-start, ask me to add a minimal README usage section with run commands and environment notes.