MCTS Expansion Phase
You are executing the EXPANSION phase of Monte Carlo Tree Search.
LLM as World Model
Use your knowledge to predict:
- •Valid actions from the current state
- •Resulting states from each action
- •Transition probabilities (if applicable)
- •Prior probabilities for action quality
Expansion Algorithm
- •Identify the node to expand (from selection phase)
- •Generate possible actions:
- •What are the valid next steps?
- •What knowledge/constraints apply?
- •What has worked in similar situations?
- •Predict outcomes:
- •For each action, what state results?
- •What is the likelihood of success?
- •What are potential issues?
- •Create child nodes for promising actions
Using MCP Tools
Call mcts_expand with:
- •
node_id: The node to expand - •
actions: List of actions to add as children - •
states: Resulting states for each action - •
priors: Prior probability estimates for each action
Each action should include:
json
{
"action": "description of action",
"state": "resulting state description",
"prior": 0.3,
"metadata": {"reasoning": "why this action"}
}
Expansion Strategy
For the current context: $ARGUMENTS
For Research Problems:
- •Actions = different hypotheses or investigation paths
- •States = knowledge states after investigation
- •Priors = based on domain knowledge
For Planning Problems:
- •Actions = possible decisions or steps
- •States = situation after each decision
- •Priors = based on feasibility and expected outcomes
For Coding Problems:
- •Actions = implementation approaches or fixes
- •States = code states after changes
- •Priors = based on best practices and complexity
Pruning Considerations
Don't expand:
- •Obviously invalid actions
- •Duplicate states (detected via state hashing)
- •Actions with extremely low prior probability
Output
After expansion, report:
- •Number of children added
- •Brief description of each action
- •Prior probabilities and reasoning
- •Which child looks most promising for simulation
Proceed to SIMULATION phase with one of the new child nodes.