# LLMMCTS Examples

This directory contains example scripts demonstrating how to use LLMMCTS for various problem types.

## Examples

1. **simple_example.jl** - Basic MCTS usage with a simple state transition function
2. **pathfinding.jl** - Grid-based pathfinding problem
3. **math_problem.jl** - Solving math problems using MCTS-guided reasoning
4. **tool_use.jl** - Coordinating with external tools (APIs, databases)
5. **chess_game.jl** - Game playing scenario (simplified chess-like)
6. **code_generation.jl** - Guiding LLM code generation
7. **reasoning.jl** - Multi-step reasoning with chain-of-thought
8. **configuration_examples.jl** - Demonstrating different MCTS configuration options

## Running Examples

```bash
julia examples/simple_example.jl
julia examples/pathfinding.jl
julia examples/configuration_examples.jl
```

## Key Concepts

### State
The state is represented as a `Dict{String, Any}` that contains all information needed for the problem.

### Transition Function
The transition function takes the current state and returns:
```julia
Dict(
    :newNodeKey => unique_id,
    :newstate => new_state_dict,
    :progressvalue => llm_estimate
)
```

### Progress Value
`progressvalue` is provided by LLM reasoning and guides the search without waiting for terminal rewards.

### State Value
`statevalue` is computed through Monte Carlo simulations and provides accurate long-term estimates.

## Configuration Parameters

- `maxiterations` - Number of MCTS iterations (default: 10)
- `explorationweight` - UCT exploration weight (default: 1.0)
- `maxSimulationDepth` - Maximum simulation rollout depth (default: 3)
- `horizontalSampleExpansionPhase` - Children per expansion (default: 3)
- `multithread` - Enable parallel simulation (default: false)
- `saveSimulatedNode` - Keep simulation nodes (default: false)

## See Also

- [README.md](../README.md) - Complete package documentation
- [workprocess.md](../workprocess.md) - Detailed technical documentation