def acquire_knowledge(self, data): self.knowledge_base.update(data)
# 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 AAGGovernance: def assess(self, problem_definition, knowledge_base): # Algorithmic governance logic return np.random.rand() def acquire_knowledge(self, data): self
class MAALearning: def adapt(self, decision, knowledge_base): # Meta-learning logic return decision + np.random.rand() aagmaal code
# Acquire knowledge aagmaal.acquire_knowledge({"data": np.random.rand()})
