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 expandactions: List of actions to add as childrenstates: Resulting states for each actionpriors: Prior probability estimates for each action
Each action should include:
{
"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.
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