mirror of
https://github.com/gsi-upm/soil
synced 2024-11-22 19:22:29 +00:00
256 lines
11 KiB
Python
256 lines
11 KiB
Python
|
import random
|
||
|
|
import networkx as nx
|
||
|
|
from soil.agents import BaseAgent, FSM, state, default_state
|
||
|
|
from scipy.spatial import cKDTree as KDTree
|
||
|
|
|
||
|
|
global betweenness_centrality_global
|
||
|
|
global degree_centrality_global
|
||
|
|
|
||
|
|
betweenness_centrality_global = None
|
||
|
|
degree_centrality_global = None
|
||
|
|
|
||
|
|
class TerroristSpreadModel(FSM):
|
||
|
|
"""
|
||
|
|
Settings:
|
||
|
|
information_spread_intensity
|
||
|
|
|
||
|
|
terrorist_additional_influence
|
||
|
|
|
||
|
|
min_vulnerability (optional else zero)
|
||
|
|
|
||
|
|
max_vulnerability
|
||
|
|
|
||
|
|
prob_interaction
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(self, environment=None, agent_id=0, state=()):
|
||
|
|
super().__init__(environment=environment, agent_id=agent_id, state=state)
|
||
|
|
|
||
|
|
global betweenness_centrality_global
|
||
|
|
global degree_centrality_global
|
||
|
|
|
||
|
|
if betweenness_centrality_global == None:
|
||
|
|
betweenness_centrality_global = nx.betweenness_centrality(self.global_topology)
|
||
|
|
if degree_centrality_global == None:
|
||
|
|
degree_centrality_global = nx.degree_centrality(self.global_topology)
|
||
|
|
|
||
|
|
self.information_spread_intensity = environment.environment_params['information_spread_intensity']
|
||
|
|
self.terrorist_additional_influence = environment.environment_params['terrorist_additional_influence']
|
||
|
|
self.prob_interaction = environment.environment_params['prob_interaction']
|
||
|
|
|
||
|
|
if self['id'] == self.civilian.id: # Civilian
|
||
|
|
self.initial_belief = random.uniform(0.00, 0.5)
|
||
|
|
elif self['id'] == self.terrorist.id: # Terrorist
|
||
|
|
self.initial_belief = random.uniform(0.8, 1.00)
|
||
|
|
elif self['id'] == self.leader.id: # Leader
|
||
|
|
self.initial_belief = 1.00
|
||
|
|
else:
|
||
|
|
raise Exception('Invalid state id: {}'.format(self['id']))
|
||
|
|
|
||
|
|
if 'min_vulnerability' in environment.environment_params:
|
||
|
|
self.vulnerability = random.uniform( environment.environment_params['min_vulnerability'], environment.environment_params['max_vulnerability'] )
|
||
|
|
else :
|
||
|
|
self.vulnerability = random.uniform( 0, environment.environment_params['max_vulnerability'] )
|
||
|
|
|
||
|
|
self.mean_belief = self.initial_belief
|
||
|
|
self.betweenness_centrality = betweenness_centrality_global[self.id]
|
||
|
|
self.degree_centrality = degree_centrality_global[self.id]
|
||
|
|
|
||
|
|
# self.state['radicalism'] = self.mean_belief
|
||
|
|
|
||
|
|
def count_neighboring_agents(self, state_id=None):
|
||
|
|
if isinstance(state_id, list):
|
||
|
|
return len(self.get_neighboring_agents(state_id))
|
||
|
|
else:
|
||
|
|
return len(super().get_agents(state_id, limit_neighbors=True))
|
||
|
|
|
||
|
|
def get_neighboring_agents(self, state_id=None):
|
||
|
|
if isinstance(state_id, list):
|
||
|
|
_list = []
|
||
|
|
for i in state_id:
|
||
|
|
_list += super().get_agents(i, limit_neighbors=True)
|
||
|
|
return [ neighbour for neighbour in _list if isinstance(neighbour, TerroristSpreadModel) ]
|
||
|
|
else:
|
||
|
|
_list = super().get_agents(state_id, limit_neighbors=True)
|
||
|
|
return [ neighbour for neighbour in _list if isinstance(neighbour, TerroristSpreadModel) ]
|
||
|
|
|
||
|
|
@state
|
||
|
|
def civilian(self):
|
||
|
|
if self.count_neighboring_agents() > 0:
|
||
|
|
neighbours = []
|
||
|
|
for neighbour in self.get_neighboring_agents():
|
||
|
|
if random.random() < self.prob_interaction:
|
||
|
|
neighbours.append(neighbour)
|
||
|
|
influence = sum( neighbour.degree_centrality for neighbour in neighbours )
|
||
|
|
mean_belief = sum( neighbour.mean_belief * neighbour.degree_centrality / influence for neighbour in neighbours )
|
||
|
|
self.initial_belief = self.mean_belief
|
||
|
|
mean_belief = mean_belief * self.information_spread_intensity + self.initial_belief * ( 1 - self.information_spread_intensity )
|
||
|
|
self.mean_belief = mean_belief * self.vulnerability + self.initial_belief * ( 1 - self.vulnerability )
|
||
|
|
|
||
|
|
if self.mean_belief >= 0.8:
|
||
|
|
return self.terrorist
|
||
|
|
|
||
|
|
@state
|
||
|
|
def leader(self):
|
||
|
|
self.mean_belief = self.mean_belief ** ( 1 - self.terrorist_additional_influence )
|
||
|
|
if self.count_neighboring_agents(state_id=[self.terrorist.id, self.leader.id]) > 0:
|
||
|
|
for neighbour in self.get_neighboring_agents(state_id=[self.terrorist.id, self.leader.id]):
|
||
|
|
if neighbour.betweenness_centrality > self.betweenness_centrality:
|
||
|
|
return self.terrorist
|
||
|
|
|
||
|
|
@state
|
||
|
|
def terrorist(self):
|
||
|
|
if self.count_neighboring_agents(state_id=[self.terrorist.id, self.leader.id]) > 0:
|
||
|
|
neighbours = self.get_neighboring_agents(state_id=[self.terrorist.id, self.leader.id])
|
||
|
|
influence = sum( neighbour.degree_centrality for neighbour in neighbours )
|
||
|
|
mean_belief = sum( neighbour.mean_belief * neighbour.degree_centrality / influence for neighbour in neighbours )
|
||
|
|
self.initial_belief = self.mean_belief
|
||
|
|
self.mean_belief = mean_belief * self.vulnerability + self.initial_belief * ( 1 - self.vulnerability )
|
||
|
|
self.mean_belief = self.mean_belief ** ( 1 - self.terrorist_additional_influence )
|
||
|
|
|
||
|
|
if self.count_neighboring_agents(state_id=self.leader.id) == 0 and self.count_neighboring_agents(state_id=self.terrorist.id) > 0:
|
||
|
|
max_betweenness_centrality = self
|
||
|
|
for neighbour in self.get_neighboring_agents(state_id=self.terrorist.id):
|
||
|
|
if neighbour.betweenness_centrality > max_betweenness_centrality.betweenness_centrality:
|
||
|
|
max_betweenness_centrality = neighbour
|
||
|
|
if max_betweenness_centrality == self:
|
||
|
|
return self.leader
|
||
|
|
|
||
|
|
def add_edge(self, G, source, target):
|
||
|
|
G.add_edge(source.id, target.id, start=self.env._now)
|
||
|
|
|
||
|
|
def link_search(self, G, node, radius):
|
||
|
|
pos = nx.get_node_attributes(G, 'pos')
|
||
|
|
nodes, coords = list(zip(*pos.items()))
|
||
|
|
kdtree = KDTree(coords) # Cannot provide generator.
|
||
|
|
edge_indexes = kdtree.query_pairs(radius, 2)
|
||
|
|
_list = [ edge[int(not edge.index(node))] for edge in edge_indexes if node in edge ]
|
||
|
|
return [ G.nodes()[index]['agent'] for index in _list ]
|
||
|
|
|
||
|
|
def social_search(self, G, node, steps):
|
||
|
|
nodes = list(nx.ego_graph(G, node, radius=steps).nodes())
|
||
|
|
nodes.remove(node)
|
||
|
|
return [ G.nodes()[index]['agent'] for index in nodes ]
|
||
|
|
|
||
|
|
|
||
|
|
class TrainingAreaModel(FSM):
|
||
|
|
"""
|
||
|
|
Settings:
|
||
|
|
training_influence
|
||
|
|
|
||
|
|
min_vulnerability
|
||
|
|
|
||
|
|
Requires TerroristSpreadModel.
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(self, environment=None, agent_id=0, state=()):
|
||
|
|
super().__init__(environment=environment, agent_id=agent_id, state=state)
|
||
|
|
self.training_influence = environment.environment_params['training_influence']
|
||
|
|
if 'min_vulnerability' in environment.environment_params:
|
||
|
|
self.min_vulnerability = environment.environment_params['min_vulnerability']
|
||
|
|
else: self.min_vulnerability = 0
|
||
|
|
|
||
|
|
@default_state
|
||
|
|
@state
|
||
|
|
def terrorist(self):
|
||
|
|
for neighbour in self.get_neighboring_agents():
|
||
|
|
if isinstance(neighbour, TerroristSpreadModel) and neighbour.vulnerability > self.min_vulnerability:
|
||
|
|
neighbour.vulnerability = neighbour.vulnerability ** ( 1 - self.training_influence )
|
||
|
|
|
||
|
|
|
||
|
|
class HavenModel(FSM):
|
||
|
|
"""
|
||
|
|
Settings:
|
||
|
|
haven_influence
|
||
|
|
|
||
|
|
min_vulnerability
|
||
|
|
|
||
|
|
max_vulnerability
|
||
|
|
|
||
|
|
Requires TerroristSpreadModel.
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(self, environment=None, agent_id=0, state=()):
|
||
|
|
super().__init__(environment=environment, agent_id=agent_id, state=state)
|
||
|
|
self.haven_influence = environment.environment_params['haven_influence']
|
||
|
|
if 'min_vulnerability' in environment.environment_params:
|
||
|
|
self.min_vulnerability = environment.environment_params['min_vulnerability']
|
||
|
|
else: self.min_vulnerability = 0
|
||
|
|
self.max_vulnerability = environment.environment_params['max_vulnerability']
|
||
|
|
|
||
|
|
@state
|
||
|
|
def civilian(self):
|
||
|
|
for neighbour_agent in self.get_neighboring_agents():
|
||
|
|
if isinstance(neighbour_agent, TerroristSpreadModel) and neighbour_agent['id'] == neighbour_agent.civilian.id:
|
||
|
|
for neighbour in self.get_neighboring_agents():
|
||
|
|
if isinstance(neighbour, TerroristSpreadModel) and neighbour.vulnerability > self.min_vulnerability:
|
||
|
|
neighbour.vulnerability = neighbour.vulnerability * ( 1 - self.haven_influence )
|
||
|
|
return self.civilian
|
||
|
|
return self.terrorist
|
||
|
|
|
||
|
|
@state
|
||
|
|
def terrorist(self):
|
||
|
|
for neighbour in self.get_neighboring_agents():
|
||
|
|
if isinstance(neighbour, TerroristSpreadModel) and neighbour.vulnerability < self.max_vulnerability:
|
||
|
|
neighbour.vulnerability = neighbour.vulnerability ** ( 1 - self.haven_influence )
|
||
|
|
return self.terrorist
|
||
|
|
|
||
|
|
|
||
|
|
class TerroristNetworkModel(TerroristSpreadModel):
|
||
|
|
"""
|
||
|
|
Settings:
|
||
|
|
sphere_influence
|
||
|
|
|
||
|
|
vision_range
|
||
|
|
|
||
|
|
weight_social_distance
|
||
|
|
|
||
|
|
weight_link_distance
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(self, environment=None, agent_id=0, state=()):
|
||
|
|
super().__init__(environment=environment, agent_id=agent_id, state=state)
|
||
|
|
|
||
|
|
self.vision_range = environment.environment_params['vision_range']
|
||
|
|
self.sphere_influence = environment.environment_params['sphere_influence']
|
||
|
|
self.weight_social_distance = environment.environment_params['weight_social_distance']
|
||
|
|
self.weight_link_distance = environment.environment_params['weight_link_distance']
|
||
|
|
|
||
|
|
@state
|
||
|
|
def terrorist(self):
|
||
|
|
self.update_relationships()
|
||
|
|
return super().terrorist()
|
||
|
|
|
||
|
|
@state
|
||
|
|
def leader(self):
|
||
|
|
self.update_relationships()
|
||
|
|
return super().leader()
|
||
|
|
|
||
|
|
def update_relationships(self):
|
||
|
|
if self.count_neighboring_agents(state_id=self.civilian.id) == 0:
|
||
|
|
close_ups = self.link_search(self.global_topology, self.id, self.vision_range)
|
||
|
|
step_neighbours = self.social_search(self.global_topology, self.id, self.sphere_influence)
|
||
|
|
search = list(set(close_ups).union(step_neighbours))
|
||
|
|
neighbours = self.get_neighboring_agents()
|
||
|
|
search = [item for item in search if not item in neighbours and isinstance(item, TerroristNetworkModel)]
|
||
|
|
for agent in search:
|
||
|
|
social_distance = 1 / self.shortest_path_length(self.global_topology, self.id, agent.id)
|
||
|
|
spatial_proximity = ( 1 - self.get_distance(self.global_topology, self.id, agent.id) )
|
||
|
|
prob_new_interaction = self.weight_social_distance * social_distance + self.weight_link_distance * spatial_proximity
|
||
|
|
if agent['id'] == agent.civilian.id and random.random() < prob_new_interaction:
|
||
|
|
self.add_edge(self.global_topology, self, agent)
|
||
|
|
break
|
||
|
|
|
||
|
|
def get_distance(self, G, source, target):
|
||
|
|
source_x, source_y = nx.get_node_attributes(G, 'pos')[source]
|
||
|
|
target_x, target_y = nx.get_node_attributes(G, 'pos')[target]
|
||
|
|
dx = abs( source_x - target_x )
|
||
|
|
dy = abs( source_y - target_y )
|
||
|
|
return ( dx ** 2 + dy ** 2 ) ** ( 1 / 2 )
|
||
|
|
|
||
|
|
def shortest_path_length(self, G, source, target):
|
||
|
|
try:
|
||
|
|
return nx.shortest_path_length(G, source, target)
|
||
|
|
except nx.NetworkXNoPath:
|
||
|
|
return float('inf')
|