MCTS Simulation Phase
You are executing the SIMULATION (rollout) phase of Monte Carlo Tree Search.
LLM as Heuristic Policy
Use your knowledge to:
- Guide the rollout toward realistic outcomes
- Evaluate terminal states with meaningful scores
- Detect dead ends early to save computation
Simulation Algorithm
- Start from the expanded node
- Rollout to terminal state:
- Select actions using LLM policy (not random!)
- Simulate state transitions
- Continue until terminal or max depth
- Evaluate the outcome:
- Success: positive reward (e.g., 1.0)
- Partial success: proportional reward (e.g., 0.5)
- Failure: zero or negative reward
Using MCP Tools
Call mcts_simulate with:
node_id: The node to simulate frommax_depth: Maximum rollout depth (default: 10)evaluation_criteria: What constitutes success
The tool returns:
terminal_state: The final state reachedreward: Numerical evaluation [0, 1]rollout_path: Sequence of actions takenreasoning: Explanation of the evaluation
Simulation Strategy
For the current context: $ARGUMENTS
Rollout Policy
Instead of random rollout, use informed policy:
- At each step, consider 2-3 likely actions
- Choose based on domain knowledge
- Prefer actions that lead to decisive outcomes
Evaluation Criteria
For Research:
- Does the path lead to valid conclusions?
- Is evidence sufficient and reliable?
- Are there logical gaps?
For Planning:
- Does the plan achieve the goal?
- Are resources within budget?
- Are there critical risks?
For Coding:
- Does the solution work correctly?
- Is the code clean and maintainable?
- Are edge cases handled?
Reward Assignment
reward = completeness * correctness * efficiency
Where each factor is in [0, 1]:
- completeness: How much of the goal is achieved
- correctness: How valid is the solution
- efficiency: How elegant/optimal is it
Output
After simulation, report:
- Terminal state reached
- Reward value with breakdown
- Key insights from the rollout
- Any observations to record
Proceed to BACKPROPAGATION with the reward.
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