Aagmaal Code |link| «UPDATED • GUIDE»

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

class AAGMAAL: def __init__(self, problem_definition): self.problem_definition = problem_definition self.knowledge_base = {} self.aag_governance = AAGGovernance() self.maal_learning = MAALearning() class AAGGovernance: def assess(self

class MAALearning: def adapt(self, decision, knowledge_base): # Meta-learning logic return decision + np.random.rand() aagmaal code

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

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

# Initialize AAGMAAL aagmaal = AAGMAAL("example problem")