Source code for Acronym based AGENT

[Deadsg]

import agi import Batman

def CAGIAgent():

class CAGIAgent:
    def __init__(self):
        self.ML = 0
        self.DL = 0
        self.NLP = 0
        self.RL = 0
        self.CV = 0
        self.KB = 0
        self.S = 0
        self.P = 0
        self.HCI = 0

    def update_values(self, ml, dl, nlp, rl, cv, kb, s, p, hci):
        self.ML = ml
        self.DL = dl
        self.NLP = nlp
        self.RL = rl
        self.CV = cv
        self.KB = kb
        self.S = s
        self.P = p
        self.HCI = hci

    def calculate_cagi(self):
        return (self.ML + self.DL + self.NLP + self.RL + self.CV + self.KB + self.S + self.P) * self.HCI

# Example Usage
cagi_system = CAGI()
cagi_system.update_values(0.8, 0.9, 0.7, 0.85, 0.75, 0.8, 0.9, 0.85, 0.95)
cagi_score = cagi_system.calculate_cagi()
print(f"The CAGI score is: {cagi_score}")

def CAGIAgent():

class CAGIAgent:
    def __init__(self):
        self.knowledge_base = {}    

    def assimilate_data(self, data):
        # Assuming data is a dictionary where keys are topics and values are information
        self.knowledge_base.update(data)

    def contextualize(self, query):
        # Simple keyword-based contextualization
        relevant_info = []
        for topic, info in self.knowledge_base.items():
            if topic.lower() in query.lower():
                relevant_info.append(info)
        return relevant_info

    def emulate_learning(self, experiences):
        # This is a very simplified example, real learning would be much more complex
        for experience in experiences:
            self.assimilate_data(experience)

    def reason(self, query):
        # Basic reasoning, you would need advanced logic for a real AGI
        relevant_info = self.contextualize(query)
        if relevant_info:
            return relevant_info[0]  # Returning the first relevant piece of information
        else:
            return "I don't have enough information to answer that."

# Example usage
cagi_agent = CAGIAgent()

# Assimilate data
data = {
    "Machine Learning": "A type of artificial intelligence that allows a system to learn from data rather than through explicit programming.",
    "Neural Networks": "A set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns."
}
cagi_agent.assimilate_data(data)

# Contextualize
query = "What is Machine Learning?"
relevant_info = cagi_agent.contextualize(query)
print(relevant_info)

# Emulate learning
experiences = [
    {"Natural Language Processing": "A field of AI that focuses on the interaction between computers and humans through natural language."}
]
cagi_agent.emulate_learning(experiences)

# Reason
query = "Tell me about Neural Networks."
response = cagi_agent.reason(query)
print(response)

def batman():

    acronym_dict = {
        'B': ['Brilliant', 'Behavior', 'Brain'],
        'A': ['Artificial', 'Advanced', 'Automated'],
        'T': ['Technology', 'Thinking', 'Transformer'],
        'M': ['Machine', 'Mind', 'Mastery'],
        'A': ['Artificial', 'Advanced', 'Automated'],
        'N': ['Network', 'Neural', 'Natural'],
        'A': ['Artificial', 'Advanced', 'Automated'],
        'G': ['General', 'Genius', 'Great'],
        'I': ['Intelligence', 'Innovative', 'Intuitive']

}