Experimental Design Optimizer

This F# program implements a Monte Carlo Tree Search (MCTS) algorithm for experimental design optimization. It uses a binary tree structure to represent and analyze experimental data, and employs the Upper Confidence Bound (UCB) algorithm for tree traversal.

Table of Contents

• Features
• Components
• Functions
• Usage
• Installation
• Contributing
• License

Features

• Monte Carlo Tree Search (MCTS) implementation
• Upper Confidence Bound (UCB) algorithm for tree traversal
• Random data generation for simulations
• Data compaction and averaging
• Integration of theoretical data points

Components

Custom Types

• NewObservation and Observation: Represent experimental data
• TreeNode and Tree: Used to build the search tree

Helper Functions

• optionAdd and optionDiv: Safe operations on optional values
• from: String formatting

Tree Operations

• TreeBuilder: Recursively constructs a binary tree from observations
• PickNode: Implements the UCB algorithm for tree traversal

Data Processing

• InitBuilder: Generates random observations
• ObsCompact: Groups and averages observations
• AddTheory: Adds theoretical test levels to the dataset

Functions

1. from
2. optionAdd
3. optionDiv
4. InitBuilder
5. TreeBuilder
6. PickNode
7. ObsCompact
8. AddTheory
9. main

Usage

The main functionality of the program:

1. Generates initial data using InitBuilder
2. Compacts the data with ObsCompact
3. Adds theoretical data points with AddTheory
4. Prints the resulting sequence

Installation

(Add installation instructions here)

Contributing

(Add contribution guidelines here)

License

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