import numpy as np
import matplotlib.pyplot as plt
from scipy.spatial import Voronoi, voronoi_plot_2d
from scipy.optimize import minimize
import gym
from stable_baselines3 import PPO
import matplotlib.patches as patches
import logging
import time
import random
from prometheus_client import start_http_server, Summary, Gauge
# Set up logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
# Prometheus metrics
REQUEST_TIME = Summary('request_processing_seconds', 'Time spent processing request')
NODE_STATUS = Gauge('node_status', 'Status of nodes')
# Start Prometheus metrics server
start_http_server(8000)
class ProcessDimension:
def __init__(self, name):
self.name = name
self.trust = 0.0
self.love = 0.0
self.thankfulness = 0.0
self.inspiration = 0.0
self.consciousness_type = 'neutral'
self.expectations = []
self.intentions = []
self.connections = []
self.level = 0
def update(self, trust=0, love=0, thankfulness=0, inspiration=0):
self.trust = max(min(self.trust + trust, 1.0), -1.0)
self.love = max(min(self.love + love, 1.0), -1.0)
self.thankfulness = max(min(self.thankfulness + thankfulness, 1.0), -1.0)
self.inspiration = max(min(self.inspiration + inspiration, 1.0), -1.0)
self.evaluate_consciousness()
def evaluate_consciousness(self):
if self.trust > 0.5 and self.love > 0.5 and self.thankfulness > 0.5:
self.consciousness_type = 'metatized'
elif self.trust < -0.5 or self.love < -0.5 or self.thankfulness < -0.5:
self.consciousness_type = 'addicted'
else:
self.consciousness_type = 'neutral'
def follow_intentions_without_imposing(self):
logging.info(f"{
self.name} follows intentions without imposing")
def detach_from_expectations_of_others(self):
logging.info(f"{
self.name} detaches from expectations of others")
def develop_own_expectations(self, expectations):
self.expectations.extend(expectations)
logging.info(f"{
self.name} develops own expectations: {expectations}")
def develop_own_intentions(self, intentions):
self.intentions.extend(intentions)
logging.info(f"{
self.name} develops own intentions: {intentions}")
def share_and_co_create(self, other):
if self.level == 7:
shared_intentions = list(set(self.intentions + other.intentions))
self.intentions = shared_intentions
other.intentions = shared_intentions
logging.info(f"{
self.name} and {
other.name} share and co-create intentions: {shared_intentions}")
def connect_and_converge(self, other):
if self.level == 8:
self.connections.append(other)
other.connections.append(self)
logging.info(f"{
self.name} and {
other.name} connect and converge")
self.emerge()
other.emerge()
def emerge(self):
if self.level == 8:
logging.info(f"{
self.name} emerges as part of a new complex structure")
def transition_to_next_level(self):
if self.level < 8:
self.level += 1
logging.info(f"{
self.name} transitions to level {self.level}")
class ModalTopologicalModule:
def __init__(self):
self.worlds = set()
self.open_sets = []
self.propositions = {}
def add_world(self, world):
self.worlds.add(world)
def add_open_set(self, open_set):
if open_set.issubset(self.worlds):
self.open_sets.append(open_set)
def define_proposition(self, name, worlds):
self.propositions[name] = set(worlds)
def is_necessary(self, proposition, world):
if proposition not in self.propositions:
return False
closure = self.closure(self.propositions[proposition])
return world in closure
def is_possible(self, proposition, world):
if proposition not in self.propositions:
return False
interior = self.interior(self.propositions[proposition])
return world in interior
def closure(self, subset):
closed_set = subset.copy()
for open_set in self.open_sets:
if not subset.isdisjoint(open_set):
closed_set |= open_set
return closed_set
def interior(self, subset):
interior_set = set()
for open_set in self.open_sets:
if open_set.issubset(subset):
interior_set |= open_set
return interior_set
def interpret_process_dimension(self, process_dimension):
world = process_dimension.level
self.add_world(world)
self.define_proposition('metatized', {7, 8})
self.define_proposition('addicted', {1, 2})
self.define_proposition('neutral', {3, 4, 5, 6})
for i in range(1, 8):
self.add_open_set({i, i+1})
is_necessarily_metatized = self.is_necessary('metatized', world)
is_possibly_metatized = self.is_possible('metatized', world)
return {
'world': world,
'necessarily_metatized': is_necessarily_metatized,
'possibly_metatized': is_possibly_metatized
}
class Agent:
def __init__(self, id):
self.id = id
self.memory = []
self.state = {}
self.process_dimension = ProcessDimension(f"Agent {id}")
def decide_action(self):
pd = self.process_dimension
if pd.trust > 0.7 and pd.love > 0.7:
action = 'collaborate'
elif pd.trust > 0.5:
action = 'communicate'
else:
action = 'move'
logging.info(f"Agent {
self.id} decided to {action}")
return action
def communicate(self, message):
logging.info(f"Agent {
self.id} communicates: {message}")
self.process_dimension.update(trust=0.1, love=0.05)
self.process_dimension.follow_intentions_without_imposing()
def collaborate(self, other_agent):
logging.info(f"Agent {
self.id} collaborates with Agent {
other_agent.id}")
self.process_dimension.update(love=0.2, thankfulness=0.15)
other_agent.process_dimension.update(love=0.2, thankfulness=0.15)
self.process_dimension.share_and_co_create(other_agent.process_dimension)
def update_memory(self, event):
self.memory.append(event)
if len(self.memory) > 100:
self.memory.pop(0)
logging.info(f"Agent {
self.id} updates memory with event: {event}")
self.process_dimension.update(inspiration=0.1)
self.process_dimension.develop_own_expectations([f"Expectation from {event}"])
class AgentBehaviorEvolution:
def __init__(self):
self.agents = [Agent(i) for i in range(10)]
def simulate(self):
for agent in self.agents:
action = agent.decide_action()
if action == 'communicate':
agent.communicate("Hello")
elif action == 'collaborate':
agent.collaborate(random.choice(self.agents))
agent.update_memory(action)
agent.process_dimension.transition_to_next_level()
def analyze_process_dimensions(self):
metatized = sum(1 for agent in self.agents if agent.process_dimension.consciousness_type == 'metatized')
addicted = sum(1 for agent in self.agents if agent.process_dimension.consciousness_type == 'addicted')
neutral = sum(1 for agent in self.agents if agent.process_dimension.consciousness_type == 'neutral')
logging.info(f"Consciousness types: Metatized: {metatized}, Addicted: {addicted}, Neutral: {neutral}")
class Governance:
def __init__(self):
self.rules = {}
self.history = []
def create_rule(self, rule):
self.rules[rule['name']] = rule
self.history.append(rule)
logging.info(f"Rule created: {rule['name']}")
def modify_rule(self, rule_name, new_rule):
if rule_name in self.rules:
self.rules[rule_name] = new_rule
self.history.append(new_rule)
logging.info(f"Rule modified: {rule_name}")
def enforce_rules(self, agent):
for rule in self.rules.values():
if rule['condition'](agent):
rule['action'](agent)
logging.info(f"Rules enforced on agent {
agent.id}")
def collective_decision_making(self, agents):
total_trust = sum(agent.process_dimension.trust for agent in agents)
decision_weight = {
agent.id: agent.process_dimension.trust / total_trust for agent in agents}
logging.info(f"Collective decision weights: {decision_weight}")
return decision_weight
def game_theory_analysis(self):
logging.info("Game theory analysis")
def self_organize(self):
logging.info("Self-organization initiated")
class Proposal:
def __init__(self, id, description):
self.id = id
self.description = description
self.votes = []
def add_vote(self, vote):
self.votes.append(vote)
logging.info(f"Vote added to proposal {
self.id}")
class DAO:
def __init__(self):
self.proposals = []
self.reputation = {}
def create_proposal(self, description):
proposal = Proposal(len(self.proposals), description)
self.proposals.append(proposal)
logging.info(f"Proposal created: {description}")
def vote(self, proposal_id, vote, agent_id):
if proposal_id < len(self.proposals):
self.proposals[proposal_id].add_vote(vote)
self.update_reputation(agent_id, vote)
def allocate_budget(self):
logging.info("Budget allocated based on proposals")
def update_reputation(self, agent_id, vote):
if agent_id not in self.reputation:
self.reputation[agent_id] = 0
self.reputation[agent_id] += vote
logging.info(f"Reputation updated for agent {agent_id}")
def resolve_disputes(self):
logging.info("Dispute resolution process")
class AIContextManager:
def __init__(self):
self.context = {}
self.actions = []
def update_context(self, new_context):
self.context.update(new_context)
logging.info("Context updated")
def choose_action(self):
if self.context.get('network_load', 0) > 80:
return 'handle_network_congestion'
if self.context.get('data_anomaly', False):
return 'anomaly_detection'
if self.context.get('average_trust', 0) < 0.3:
return 'build_trust'
if self.context.get('average_love', 0) < 0.3:
return 'foster_collaboration'
return 'default_action'
def optimize_agency(self, agents):
for agent in agents:
if agent.process_dimension.consciousness_type == 'addicted':
agent.process_dimension.update(trust=0.1, love=0.1)
agent.process_dimension.detach_from_expectations_of_others()
logging.info("Optimizing agency")
def disincentivize_hoarding(self, agents):
for agent in agents:
if agent.process_dimension.trust > 0.8 and agent.process_dimension.love < 0.3:
agent.process_dimension.update(trust=-0.1, love=0.1)
agent.process_dimension.develop_own_intentions(["Share resources"])
logging.info("Disincentivizing hoarding")
class DistributedLedger:
def __init__(self):
self.ledger = []
self.nodes = []
def add_node(self, node):
self.nodes.append(node)
logging.info(f"Node added: {node}")
def add_transaction(self, transaction):
self.ledger.append(transaction)
logging.info(f"Transaction added: {transaction}")
def replicate_data(self):
for node in self.nodes:
logging.info(f"Data replicated to node: {node}")
class ParametricModule:
def __init__(self, params):
self.params = params
def display(self):
raise NotImplementedError("This method should be implemented by subclasses")
class VoronoiModule(ParametricModule):
def display(self):
points = np.random.rand(self.params['num_points'], 2)
vor = Voronoi(points)
fig, ax = plt.subplots()
voronoi_plot_2d(vor, ax=ax, show_points=False, show_vertices=False, line_colors='black')
ax.set_aspect('equal')
plt.title("Voronoi-Based Modular Component")
plt.xlabel("Width")
plt.ylabel("Height")
plt.show()
class TrussGridShellModule(ParametricModule):
def display(self):
x = np.linspace(-1, 1, self.params['grid_size'])
y = np.linspace(-1, 1, self.params['grid_size'])
x, y = np.meshgrid(x, y)
z = np.sin(np.pi * x) * np.sin(np.pi * y)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot_wireframe(x, y, z, color='black')
plt.title("Adaptive Truss and Grid Shell Module")
plt.show()
class OrigamiModule(ParametricModule):
def display(self):
def origami_fold(x, y, fold_line):
x_folded = np.where(x < fold_line, x, 2 * fold_line - x)
return x_folded, y
x = np.linspace(-1, 1, 100)
y = np.sin(3 * np.pi * x)
fold_line = self.params['fold_line']
x_folded, y_folded = origami_fold(x, y, fold_line)
plt.plot(x, y, 'b', label='Original')
plt.plot(x_folded, y_folded, 'r--', label='Folded')
plt.axvline(x=fold_line, color='k', linestyle='--')
plt.legend()
plt.title("Foldable Origami-Based Structure")
plt.xlabel("Width")
plt.ylabel("Height")
plt.show()
class SelfHealingMaterial(ParametricModule):
def display(self):
fig, ax = plt.subplots()
ax.set_aspect('equal')
material = patches.Rectangle((0, 0), 4, 4, fill=True, color='lightgray')
ax.add_patch(material)
crack = patches.FancyBboxPatch((1, 1), 2, 0.2, boxstyle="round,pad=0.05", fill=True, color='darkgray')
ax.add_patch(crack)
healing = patches.FancyBboxPatch((1.5, 1.05), 1, 0.1, boxstyle="round,pad=0.05", fill=True, color='lightgray')
ax.add_patch(healing)
plt.title("Self-Healing Material")
plt.xlim(-1, 5)
plt.ylim(-1, 5)
plt.show()
class ModalLogic:
def __init__(self):
self.possibilities = {}
self.necessities = {}
def possible(self, module1, module2):
if module1 not in self.possibilities:
self.possibilities[module1] = set()
self.possibilities[module1].add(module2)
def necessary(self, module1, module2):
if module1 not in self.necessities:
self.necessities[module1] = set()
self.necessities[module1].add(module2)
def display_combinations(self):
print("Possibilities:")
for module, connections in self.possibilities.items():
print(f"{module} can possibly connect to: {', '.join(connections)}")
print("\nNecessities:")
for module, connections in self.necessities.items():
print(f"{module} must connect to: {', '.join(connections)}")
class TopologicalSpace:
def __init__(self, points):
self.points = points
self.open_sets = []
def add_open_set(self, points):
self.open_sets.append(points)
def display_topology(self):
fig, ax = plt.subplots()
ax.scatter(self.points[:, 0], self.points[:, 1], c='black', s=10)
for open_set in self.open_sets:
ax.scatter(open_set[:, 0], open_set[:, 1], c='red', s=30, alpha=0.5)
plt.title("Topological Space")
plt.xlabel("X")
plt.ylabel("Y")
plt.show()
class HierarchicalStructure:
def __init__(self):
self.layers = {}
def add_module_to_layer(self, layer, module):
if layer not in self.layers:
self.layers[layer] = []
self.layers[layer].append(module)
def display_structure(self):
for layer, modules in self.layers.items():
print(f"Layer {layer}:")
for module in modules:
print(f" {module}")
class CustomEnv(gym.Env):
def __init__(self):
super(CustomEnv, self).__init__()
self.action_space = gym.spaces.Discrete(3)
self.observation_space = gym.spaces.Box(low=0, high=1, shape=(4,), dtype=np.float32)
def reset(self):
return np.random.rand(4)
def step(self, action):
obs = np.random.rand(4)
reward = np.random.rand()
done = np.random.choice([True, False])
return obs, reward, done, {}
class AgentManager:
def __init__(self):
self.env = CustomEnv()
self.model = PPO("MlpPolicy", self.env, verbose=1)
def train(self, total_timesteps):
self.model.learn(total_timesteps=total_timesteps)
def predict(self, observation):
action, _states = self.model.predict(observation)
return action
class ParametricModuleSystem:
def __init__(self):
self.modules = []
self.modal_logic = ModalLogic()
self.topology = TopologicalSpace(points=np.random.rand(100, 2))
self.hierarchy = HierarchicalStructure()
self.agent_manager = AgentManager()
def add_module(self, module):
self.modules.append(module)
def optimize_structure(self, objective_function, initial_params, constraints):
result = minimize(objective_function, initial_params, constraints=constraints)
return result.x
def display_system(self):
print("Modules:")
for module in self.modules:
module.display()
print("\nModal Logic Combinations:")
self.modal_logic.display_combinations()
print("\nTopological Semantics:")
self.topology.display_topology()
print("\nHierarchical Structure:")
self.hierarchy.display_structure()
def run(self):
# Example usage
voronoi_module = VoronoiModule({'num_points': 20})
truss_module = TrussGridShellModule({'grid_size': 10})
origami_module = OrigamiModule({'fold_line': 0.5})
self_healing_material = SelfHealingMaterial({})
self.add_module(voronoi_module)
self.add_module(truss_module)
self.add_module(origami_module)
self.add_module(self_healing_material)
self.modal_logic.possible("Voronoi Module", "Truss Module")
self.modal_logic.necessary("Voronoi Module", "Origami Module")
self.topology.add_open_set(np.random.rand(10, 2))
self.topology.add_open_set(np.random.rand(15, 2))
self.hierarchy.add_module_to_layer(1, "Voronoi Module")
self.hierarchy.add_module_to_layer(2, "Truss Module")
self.hierarchy.add_module_to_layer(3, "Origami Module")
self.hierarchy.add_module_to_layer(4, "Self-Healing Material")
initial_params = np.array([1.0, 2.0, 3.0, 4.0])
constraints = [{'type': 'ineq', 'fun': lambda x: x - 0.1}]
optimized_params = self.optimize_structure(objective_function, initial_params, constraints)
print("Optimized Parameters:", optimized_params)
self.agent_manager.train(total_timesteps=1000)
observation = self.agent_manager.env.reset()
action = self.agent_manager.predict(observation)
print("Predicted Action:", action)
self.display_system()
def objective_function(params):
stability = params[0] * params[1]
efficiency = np.sum(params)
return stability / efficiency
# Main execution
if __name__ == "__main__":
parametric_system = ParametricModuleSystem()
parametric_system.run()