MCTS
Leverages Monte Carlo Tree Search and Bayesian analysis to provide AI-assisted reasoning and deep exploration of topics.
About
Provides a Model Context Protocol (MCP) server that utilizes an Advanced Bayesian Monte Carlo Tree Search (MCTS) engine, enabling AI assistants like Claude to perform in-depth analysis and reasoning. By exploring various angles and interpretations through multiple iterations, the MCTS algorithm delivers insightful analyses that evolve continuously, offering a powerful tool for understanding complex subjects.
Key Features
- Employs Bayesian MCTS for probabilistic exploration and exploitation balancing.
- 2 GitHub stars
- Supports multi-iteration analysis with multiple simulations per iteration.
- Classifies generated thoughts into philosophical approaches.
- Offers both Thompson Sampling and UCT for node selection.
- Maintains state persistence, remembering key results and approaches.
Use Cases
- Deep, explorative analysis of complex topics and questions.
- AI-assisted reasoning and problem-solving.
- Enhanced decision-making through systematic exploration of possibilities.
Sponsored