Self Aware Settlement

[Dante Monson]

  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()