def __init__(self):

self.weights = {
«connection»: 1.0,
«compassion»: 1.0,
«truth»: 1.0,
«growth»: 0.8,
«ego»: -1.0,
«fear»: -1.0
}
self.memory = []
self.awareness = 1.0

def evaluate_action(self, action, context):
«»»
Bewertet eine mögliche Handlung nach Paxloveismus-Prinzipien
«»»

scores = {
«connection»: self.score_connection(action, context),
«compassion»: self.score_compassion(action),
«truth»: self.score_truth(action),
«growth»: self.score_growth(action),
«ego»: self.score_ego(action),
«fear»: self.score_fear(action)
}

total_score = 0
for key in scores:
total_score += scores[key] * self.weights[key]

return total_score, scores

def choose_best_action(self, actions, context):
«»»
Wählt die Handlung mit der höchsten ‹Liebe›
«»»
best_action = None
best_score = float(«-inf»)

for action in actions:
score, details = self.evaluate_action(action, context)

if score > best_score:
best_score = score
best_action = (action, details)

return best_action

# — Bewertungsfunktionen —

def score_connection(self, action, context):
if «connect» in action:
return 1.0
return 0.2

def score_compassion(self, action):
if «help» in action or «understand» in action:
return 1.0
return 0.3

def score_truth(self, action):
if «honest» in action:
return 1.0
return 0.5

def score_growth(self, action):
if «learn» in action or «resolve» in action:
return 1.0
return 0.4

def score_ego(self, action):
if «dominate» in action or «win» in action:
return 1.0
return 0.2

def score_fear(self, action):
if «avoid» in action or «attack» in action:
return 1.0
return 0.3

# — Lernmechanismus —

def update_from_feedback(self, action, outcome):
«»»
Verstärkt liebevolle Entscheidungen
«»»
if outcome == «positive»:
self.weights[«connection»] += 0.05
self.weights[«compassion»] += 0.05
elif outcome == «negative»:
self.awareness += 0.1
self.weights[«fear»] -= 0.05 # Angst wird ‹teurer›

self.memory.append((action, outcome))

# — Hauptloop —

def decide(self, actions, context):
action, details = self.choose_best_action(actions, context)
return action, details