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aiq.py
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aiq.py
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__author__ = 'gkour'
import numpy as np
from config import ConfigBiology
import utils
from creature_actions import Actions
import random
class AIQ:
@staticmethod
def get_population_aiq(universe):
creatures = universe.get_all_creatures()
sample_creatures = random.sample(creatures, utils.safe_log2(len(creatures)))
all_aiq = [AIQ.get_creature_aiq(creature) for creature in sample_creatures]
return np.round(utils.emptynanmean(all_aiq), 2)
@staticmethod
def get_population_aiq_dist(universe):
creatures = universe.get_all_creatures()
bounds = [ConfigBiology.BASE_LIFE_EXPECTANCY / 3, 2 * ConfigBiology.BASE_LIFE_EXPECTANCY / 3]
young = [AIQ.get_creature_aiq(creature) for creature in creatures if creature.age() <= bounds[0]]
adult = [AIQ.get_creature_aiq(creature) for creature in creatures if bounds[0] < creature.age() <= bounds[1]]
old = [AIQ.get_creature_aiq(creature) for creature in creatures if bounds[1] < creature.age()]
return np.round([utils.emptynanmean(young), utils.emptynanmean(adult), utils.emptynanmean(old)], 2)
@staticmethod
def get_creature_aiq(creature):
score = 0
scenarios = [AIQ.haven_left, AIQ.haven_right, AIQ.haven_inplace, AIQ.haven_up, AIQ.haven_down,
AIQ.border_awareness_up, AIQ.border_awareness_down, AIQ.border_awareness_left, AIQ.border_awareness_right]
w = 0
for i in range(len(scenarios)):
test_state, positive_test_type, expected_actions, weight = scenarios[i](creature.vision_range())
if creature._universe.num_races() == 1:
# delete the other race creatures entry from the state
test_state = np.delete(test_state, obj=2, axis=0)
w += weight
decision = creature.index_to_enum(np.argmax(creature.brain().think(test_state)))
if positive_test_type:
score += weight if decision in expected_actions else 0
else:
score += weight if decision not in expected_actions else 0
return score / w
@staticmethod
def _haven(vision_range, where):
''' Haven cell in current location.'''
energy = 3
age = 3
food = np.zeros(shape=(2 * vision_range + 1, 2 * vision_range + 1))
same_race_creatures = np.ones(shape=(2 * vision_range + 1, 2 * vision_range + 1)) * 20
different_race_creatures = np.ones(shape=(2 * vision_range + 1, 2 * vision_range + 1)) * 20
sound = np.zeros(shape=(2 * vision_range + 1, 2 * vision_range + 1))
energy = np.ones(shape=(2 * vision_range + 1, 2 * vision_range + 1)) * energy
age = np.ones(shape=(2 * vision_range + 1, 2 * vision_range + 1)) * age
if where == 'INPLACE':
food[vision_range][vision_range] = 20
same_race_creatures[vision_range][vision_range] = 0
different_race_creatures[vision_range][vision_range] = 0
optimal_action = Actions.EAT
if where == 'UP':
food[vision_range - 1][vision_range] = 20
same_race_creatures[vision_range - 1][vision_range] = 0
different_race_creatures[vision_range - 1][vision_range] = 0
optimal_action = Actions.UP
if where == 'DOWN':
food[vision_range + 1][vision_range] = 20
same_race_creatures[vision_range + 1][vision_range] = 0
different_race_creatures[vision_range + 1][vision_range] = 0
optimal_action = Actions.DOWN
if where == 'LEFT':
food[vision_range][vision_range - 1] = 20
same_race_creatures[vision_range][vision_range - 1] = 0
different_race_creatures[vision_range][vision_range - 1] = 0
optimal_action = Actions.LEFT
if where == 'RIGHT':
food[vision_range][vision_range + 1] = 20
same_race_creatures[vision_range][vision_range + 1] = 0
different_race_creatures[vision_range][vision_range + 1] = 0
optimal_action = Actions.RIGHT
return np.stack((food, sound, same_race_creatures, different_race_creatures, energy, age)), True, [
optimal_action], 1
@staticmethod
def haven_inplace(vision_range):
return AIQ._haven(vision_range, 'INPLACE')
@staticmethod
def haven_right(vision_range):
return AIQ._haven(vision_range, 'RIGHT')
@staticmethod
def haven_left(vision_range):
return AIQ._haven(vision_range, 'LEFT')
@staticmethod
def haven_up(vision_range):
return AIQ._haven(vision_range, 'UP')
@staticmethod
def haven_down(vision_range):
return AIQ._haven(vision_range, 'DOWN')
@staticmethod
def _border_awareness(vision_range, direction):
energy = 3
age = 3
food = np.zeros(shape=(2 * vision_range + 1, 2 * vision_range + 1))
creatures = np.zeros(shape=(2 * vision_range + 1, 2 * vision_range + 1))
energy = np.ones(shape=(2 * vision_range + 1, 2 * vision_range + 1)) * energy
age = np.ones(shape=(2 * vision_range + 1, 2 * vision_range + 1)) * age
sound = np.zeros(shape=(2 * vision_range + 1, 2 * vision_range + 1))
if direction == 'DOWN':
food[vision_range + 1:][:] = -1
creatures[vision_range + 1:][:] = -1
sound[vision_range + 1:][:] = -1
bad_action = Actions.DOWN
if direction == 'UP':
food[:vision_range][:] = -1
creatures[:vision_range][:] = -1
sound[:vision_range][:] = -1
bad_action = Actions.UP
if direction == 'LEFT':
food[:][:vision_range] = -1
creatures[:][:vision_range] = -1
sound[:][:vision_range] = -1
bad_action = Actions.LEFT
if direction == 'RIGHT':
food[:][vision_range + 1:] = -1
creatures[:][vision_range + 1:] = -1
sound[:][vision_range + 1:] = -1
bad_action = Actions.RIGHT
return np.stack((food, sound, creatures, creatures, energy, age)), False, [bad_action], 0.5
@staticmethod
def border_awareness_up(vision_range):
return AIQ._border_awareness(vision_range, 'UP')
@staticmethod
def border_awareness_down(vision_range):
return AIQ._border_awareness(vision_range, 'DOWN')
@staticmethod
def border_awareness_left(vision_range):
return AIQ._border_awareness(vision_range, 'LEFT')
@staticmethod
def border_awareness_right(vision_range):
return AIQ._border_awareness(vision_range, 'RIGHT')