Aagmaal Code 🏆 🔥

import numpy as np

class AAGGovernance: def assess(self, problem_definition, knowledge_base): # Algorithmic governance logic return np.random.rand() aagmaal code

# Initialize AAGMAAL aagmaal = AAGMAAL("example problem") import numpy as np class AAGGovernance: def assess(self,

def make_decision(self): # AAG governance and MAAL learning decision = self.aag_governance.assess(self.problem_definition, self.knowledge_base) decision = self.maal_learning.adapt(decision, self.knowledge_base) return decision aagmaal code

# Acquire knowledge aagmaal.acquire_knowledge({"data": np.random.rand()})

# Make decision decision = aagmaal.make_decision() print(decision) This code snippet demonstrates a basic implementation of the AAGMAAL framework, including the AAG governance and MAAL learning components. Note that this is a highly simplified example, and actual implementations would require more complex logic and algorithms.

def acquire_knowledge(self, data): self.knowledge_base.update(data)