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| from typing import Dict, Any, List | |
| import torch | |
| import torch.nn as nn | |
| class TheoryOfMind: | |
| def __init__(self): | |
| self.mental_state_model = nn.Sequential( | |
| nn.Linear(768, 256), | |
| nn.ReLU(), | |
| nn.Linear(256, 128) | |
| ) | |
| self.belief_system = {} | |
| def model_agent_mind(self, | |
| agent_data: Dict[str, Any], | |
| context: Dict[str, Any] = None) -> Dict[str, Any]: | |
| # Theory of Mind implementation | |
| mental_state = self._process_mental_state(agent_data) | |
| beliefs = self._update_belief_system(mental_state, context) | |
| return { | |
| 'mental_state': mental_state, | |
| 'beliefs': beliefs, | |
| 'predicted_behavior': self._predict_behavior(mental_state, beliefs) | |
| } | |
| def _process_mental_state(self, agent_data: Dict[str, Any]) -> Any: | |
| # Mental state processing implementation | |
| return {} # Return empty dict instead of None | |
| def _update_belief_system(self, mental_state: Any, context: Dict[str, Any] = None) -> Dict[str, Any]: | |
| # Belief system update implementation | |
| return {} # Return empty dict instead of None | |
| def _predict_behavior(self, mental_state: Any, beliefs: Dict[str, Any]) -> Any: | |
| # Behavior prediction implementation | |
| return {} # Return empty dict instead of None | |