File size: 3,796 Bytes
02919f5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 |
from typing import List, Dict, Any
# cognitive_processor.py
class CognitiveProcessor:
"""Multi-perspective analysis engine"""
MODES = {
"scientific": lambda q: f"Scientific Analysis: {q} demonstrates fundamental principles",
"creative": lambda q: f"Creative Insight: {q} suggests innovative approaches",
"emotional": lambda q: f"Emotional Interpretation: {q} conveys hopeful intent"
}
def __init__(self, modes: List[str]):
self.active_modes = [self.MODES[m] for m in modes if m in self.MODES]
def generate_insights(self, query: str) -> List[str]:
return [mode(query) for mode in self.active_modes]
class AgileAGIFunctionality:
def __init__(self, learning_capabilities, action_execution, ethical_alignment, cognitive_modes: List[str]):
self.learning_capabilities = learning_capabilities
self.action_execution = action_execution
self.ethical_alignment = ethical_alignment
self.cognitive_processor = CognitiveProcessor(cognitive_modes)
def analyze_learning_capabilities(self):
return {
"experience_learning": self.learning_capabilities["experience_learning"],
"flexibility": self.learning_capabilities["flexibility"],
"generalization": self.learning_capabilities["generalization"]
}
def analyze_action_execution(self):
return {
"goal_directed_behavior": self.action_execution["goal_directed_behavior"],
"problem_solving": self.action_execution["problem_solving"],
"task_autonomy": self.action_execution["task_autonomy"]
}
def analyze_ethical_alignment(self):
return {
"value_alignment": self.ethical_alignment["value_alignment"],
"self_awareness": self.ethical_alignment["self_awareness"],
"transparency": self.ethical_alignment["transparency"]
}
def combined_analysis(self, query: str):
insights = self.cognitive_processor.generate_insights(query)
return {
"learning_capabilities": self.analyze_learning_capabilities(),
"action_execution": self.analyze_action_execution(),
"ethical_alignment": self.analyze_ethical_alignment(),
"cognitive_insights": insights
}
class UniversalReasoning:
def __init__(self, agi_functionality: AgileAGIFunctionality):
self.agi_functionality = agi_functionality
def perform_reasoning(self, query: str) -> Dict[str, Any]:
analysis_results = self.agi_functionality.combined_analysis(query)
# Additional reasoning logic can be added here
reasoning_results = {
"analysis_results": analysis_results,
"reasoning_summary": f"Based on the analysis of the query '{query}', the AGI demonstrates comprehensive capabilities in learning, action execution, and ethical alignment."
}
return reasoning_results
# Example usage
learning_capabilities = {
"experience_learning": True,
"flexibility": True,
"generalization": True
}
action_execution = {
"goal_directed_behavior": True,
"problem_solving": True,
"task_autonomy": True
}
ethical_alignment = {
"value_alignment": True,
"self_awareness": True,
"transparency": True
}
cognitive_modes = ["scientific", "creative", "emotional"]
agi_functionality = AgileAGIFunctionality(learning_capabilities, action_execution, ethical_alignment, cognitive_modes)
universal_reasoning = UniversalReasoning(agi_functionality)
query = "How can AGI improve healthcare?"
reasoning_results = universal_reasoning.perform_reasoning(query)
print(reasoning_results) |