Try and create a python script - (OBJECTIVE: Chess AI) PROJECT, with precision and focus on each part respectively. Outline:

At the end of the first message you'll have to output; "Would you like me to start with '[name].py'?"

Title: ["Correct Moves"]
Exclude["Any simplified attempts is excluded."]

Breakdown:

### Project: **SEARCH: APIHole Engine**

---

#### **Project Overview**

SEARCH: APIHole Engine is an advanced chess engine built with a hyper-efficient search algorithm aimed at exploring every possible move and response at an overzealous depth. Its performance focuses on relentless search tree expansion, pruning, and pattern recognition to achieve high computational precision.

---

#### **Components**

1. **Core Search Algorithm**:  
   - **Minimax Optimization**: With **Alpha-Beta Pruning**, a deep recursive tree is searched, dramatically reducing the nodes by cutting off unworthy branches.
   - **Iterative Deepening**: Enhances time control by progressively deepening the search.
   - **Quiescence Search**: Extends evaluation beyond unstable positions to avoid horizon effects.
   - **Transposition Tables**: Caches already explored positions, minimizing redundant calculations.
   - **Null Move Pruning**: Aggressively cuts down search depth when a "null" (no action) move shows positional dominance.

2. **Heuristics**:
   - **Evaluation Function**: Captures material balance, positional advantage, king safety, pawn structure, space, and control.
   - **Piece-Square Tables**: For quick lookup of relative piece strength by position.
   - **Late Move Reductions**: Prunes late moves of no significant advantage.
   - **Forward Pruning (Futility Pruning)**: Disregards moves unlikely to improve position.

3. **Move Ordering**:  
   - **Killer Move Heuristic**: Keeps track of moves that frequently cause beta-cutoffs for fast exploration.
   - **History Heuristic**: Orders moves by the frequency of success in previous positions.
   - **Principal Variation Search**: Focuses deeper on the most promising move (the principal variation).

4. **Hashing & Caching**:
   - **Zobrist Hashing**: Unique position encoding for fast retrieval of previously seen board states.
   - **Deep Cache Layers**: Multi-layered caching for storing deep evaluations to speed up re-evaluations.
   - **Collision Avoidance Mechanisms**: Handles hash collisions efficiently with a minimal impact on performance.

5. **Parallel Processing**:
   - **Multi-Threading**: Executes parallel search threads, splitting the search tree across available CPU cores.
   - **Distributed Computing Support**: Enables clustering for distributed search across multiple systems.

6. **Opening Book & Endgame Tablebases**:
   - **Polyglot Opening Book**: Integrates with standard opening theory to give the engine a fast start in common positions.
   - **Syzygy Endgame Tablebases**: Perfect play database for all possible endgames with up to 7 pieces.

7. **Move Generation**:
   - **Bitboard Representation**: Optimized move generation using 64-bit integers for fast board manipulation.
   - **Move Legality Checker**: Rapidly verifies move legality using pre-computed attack patterns.

8. **Advanced Time Management**:
   - **Dynamic Time Allocation**: Adjusts search depth and evaluation time based on position complexity and remaining time.
   - **Pondering**: Predicts the opponent's move and continues thinking during their turn.

9. **API & Interface**:
   - **UCI (Universal Chess Interface) Protocol**: Provides a standard interface for communication with chess GUIs.
   - **RESTful API for Distributed Engines**: Exposes engine search capabilities via an API to leverage cloud-based resources.
  
10. **Logging & Debugging Tools**:
    - **Move Logging**: Logs all positions evaluated, depth reached, and move choices for analysis.
    - **Search Visualization Tool**: Graphically displays the search tree in real-time to help understand the engine's thought process.

11. **Machine Learning Integration** (Optional):
    - **Reinforcement Learning Module**: Learns to evaluate positions better over time by training on self-play games.
    - **Neural Network Evaluation**: Utilizes a lightweight neural net for positional evaluation, feeding into the traditional search process.

---

#### **Project Structure**

```plaintext
/SEARCH-APIHole
│
├── src/
│   ├── engine/
│   │   ├── search.py            # Main search engine implementation
│   │   ├── evaluation.py        # Evaluation function heuristics
│   │   ├── transposition.py     # Transposition table logic
│   │   └── time_manager.py      # Time management for the engine
│   ├── interface/
│   │   ├── uci_interface.py     # UCI protocol implementation
│   │   └── api.py               # RESTful API for distributed search
│   ├── tools/
│   │   ├── log_analysis.py      # Logging and debugging utilities
│   │   └── visualization.py     # Search tree visualization
│   └── tests/
│       ├── test_search.py       # Unit tests for the search algorithm
│       └── test_evaluation.py   # Unit tests for evaluation functions
│
├── opening_books/
│   └── polyglot.bin             # Precompiled opening book file
├── tablebases/
│   └── syzygy/                  # Endgame tablebases
├── docs/
│   ├── overview.md              # Project overview and design document
│   └── api_docs.md              # API documentation
└── config/
    └── engine_config.json       # Config file for engine settings
```

---

#### **Tech Stack**

- **Programming Language**: Python / C++ hybrid
- **Concurrency**: OpenMP / Python threading
- **Interface**: REST API + UCI
- **Opening/Endgame**: Polyglot, Syzygy
- **Libraries**: 
  - NumPy for matrix calculations (bitboards, neural nets)
  - Flask (or FastAPI) for the API
  - TensorFlow / PyTorch for machine learning modules