320 lines
12 KiB
Python
320 lines
12 KiB
Python
"""Personality and skill system for agents in the Village Simulation.
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Each agent has unique personality traits that affect their behavior:
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- How much they value wealth vs. survival resources
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- What activities they prefer (hunting, gathering, trading)
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- How willing they are to take risks
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- How much they hoard vs. trade
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Agents also develop skills over time based on their actions:
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- Skills improve with practice
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- Better skills = better outcomes
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This creates emergent professions:
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- Hunters: High hunting skill, prefer meat production
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- Gatherers: High gathering skill, prefer berries/water/wood
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- Traders: High trading skill, focus on buy low / sell high arbitrage
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"""
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import random
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from dataclasses import dataclass, field
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from enum import Enum
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from typing import Optional
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class ProfessionType(Enum):
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"""Emergent profession types based on behavior patterns."""
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HUNTER = "hunter"
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GATHERER = "gatherer"
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WOODCUTTER = "woodcutter"
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TRADER = "trader"
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GENERALIST = "generalist"
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@dataclass
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class PersonalityTraits:
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"""Unique personality traits that affect agent behavior.
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These are set at birth and don't change during the agent's life.
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They create natural diversity in the population.
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"""
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# How much the agent values accumulating wealth (0.1 = minimal, 0.9 = greedy)
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wealth_desire: float = 0.3
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# How much the agent hoards resources vs trades them (0.1 = trades freely, 0.9 = hoards)
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hoarding_rate: float = 0.5
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# Willingness to take risks (0.1 = very cautious, 0.9 = risk-taker)
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# Affects: hunting vs gathering preference, price decisions
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risk_tolerance: float = 0.5
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# Sensitivity to good/bad deals (0.5 = not picky, 1.5 = very price conscious)
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price_sensitivity: float = 1.0
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# Activity biases - how much the agent prefers each activity
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# Higher values = more likely to choose this activity
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# These create "profession tendencies"
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hunt_preference: float = 1.0 # Preference for hunting
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gather_preference: float = 1.0 # Preference for gathering
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woodcut_preference: float = 1.0 # Preference for wood
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trade_preference: float = 1.0 # Preference for trading/market
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# How social/market-oriented the agent is
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# High = frequent market visits, buys more from others
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# Low = self-sufficient, prefers to produce own resources
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market_affinity: float = 0.5
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def to_dict(self) -> dict:
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return {
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"wealth_desire": round(self.wealth_desire, 2),
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"hoarding_rate": round(self.hoarding_rate, 2),
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"risk_tolerance": round(self.risk_tolerance, 2),
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"price_sensitivity": round(self.price_sensitivity, 2),
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"hunt_preference": round(self.hunt_preference, 2),
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"gather_preference": round(self.gather_preference, 2),
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"woodcut_preference": round(self.woodcut_preference, 2),
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"trade_preference": round(self.trade_preference, 2),
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"market_affinity": round(self.market_affinity, 2),
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}
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@dataclass
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class Skills:
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"""Skills that improve with practice.
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Each skill affects the outcome of related actions.
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Skills increase slowly through practice (use it or lose it).
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"""
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# Combat/hunting skill - affects hunt success rate
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hunting: float = 1.0
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# Foraging skill - affects gather output quantity
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gathering: float = 1.0
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# Woodcutting skill - affects wood output
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woodcutting: float = 1.0
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# Trading skill - affects prices (buy lower, sell higher)
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trading: float = 1.0
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# Crafting skill - affects craft quality/success
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crafting: float = 1.0
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# Skill improvement rate per action
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IMPROVEMENT_RATE: float = 0.02
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# Skill decay rate per turn (use it or lose it, gentle decay)
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DECAY_RATE: float = 0.001
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# Maximum skill level
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MAX_SKILL: float = 2.0
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# Minimum skill level
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MIN_SKILL: float = 0.5
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def improve(self, skill_name: str, amount: Optional[float] = None, learning_modifier: float = 1.0) -> None:
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"""Improve a skill through practice.
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Args:
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skill_name: Name of the skill to improve
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amount: Base improvement amount (defaults to IMPROVEMENT_RATE)
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learning_modifier: Age-based modifier (young learn faster, old learn slower)
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"""
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if amount is None:
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amount = self.IMPROVEMENT_RATE
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# Apply learning modifier (young agents learn faster)
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amount = amount * learning_modifier
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if hasattr(self, skill_name):
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current = getattr(self, skill_name)
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new_value = min(self.MAX_SKILL, current + amount)
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setattr(self, skill_name, new_value)
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def decay_all(self) -> None:
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"""Apply gentle decay to all skills (use it or lose it)."""
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for skill_name in ['hunting', 'gathering', 'woodcutting', 'trading', 'crafting']:
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current = getattr(self, skill_name)
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new_value = max(self.MIN_SKILL, current - self.DECAY_RATE)
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setattr(self, skill_name, new_value)
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def get_primary_skill(self) -> tuple[str, float]:
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"""Get the agent's highest skill and its name."""
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skills = {
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'hunting': self.hunting,
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'gathering': self.gathering,
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'woodcutting': self.woodcutting,
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'trading': self.trading,
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'crafting': self.crafting,
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}
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best_skill = max(skills, key=skills.get)
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return best_skill, skills[best_skill]
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def to_dict(self) -> dict:
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return {
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"hunting": round(self.hunting, 3),
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"gathering": round(self.gathering, 3),
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"woodcutting": round(self.woodcutting, 3),
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"trading": round(self.trading, 3),
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"crafting": round(self.crafting, 3),
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}
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def generate_random_personality(archetype: Optional[str] = None) -> PersonalityTraits:
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"""Generate random personality traits.
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If archetype is specified, traits will be biased towards that profession:
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- "hunter": High risk tolerance, high hunt preference
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- "gatherer": Low risk tolerance, high gather preference
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- "trader": High wealth desire, high market affinity, high trade preference
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- "hoarder": High hoarding rate, low market affinity
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- None: Fully random
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Returns a PersonalityTraits instance with randomized values.
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"""
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# Start with base random values
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traits = PersonalityTraits(
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wealth_desire=random.uniform(0.1, 0.9),
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hoarding_rate=random.uniform(0.2, 0.8),
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risk_tolerance=random.uniform(0.2, 0.8),
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price_sensitivity=random.uniform(0.6, 1.4),
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hunt_preference=random.uniform(0.5, 1.5),
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gather_preference=random.uniform(0.5, 1.5),
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woodcut_preference=random.uniform(0.5, 1.5),
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trade_preference=random.uniform(0.5, 1.5),
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market_affinity=random.uniform(0.2, 0.8),
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)
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# Apply archetype biases
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if archetype == "hunter":
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traits.hunt_preference = random.uniform(1.3, 2.0)
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traits.risk_tolerance = random.uniform(0.6, 0.9)
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traits.gather_preference = random.uniform(0.3, 0.7)
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elif archetype == "gatherer":
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traits.gather_preference = random.uniform(1.3, 2.0)
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traits.risk_tolerance = random.uniform(0.2, 0.5)
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traits.hunt_preference = random.uniform(0.3, 0.7)
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elif archetype == "trader":
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traits.trade_preference = random.uniform(1.5, 2.5)
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traits.market_affinity = random.uniform(0.7, 0.95)
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traits.wealth_desire = random.uniform(0.6, 0.95)
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traits.price_sensitivity = random.uniform(1.1, 1.6)
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traits.hoarding_rate = random.uniform(0.1, 0.4) # Traders sell!
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# Traders don't hunt/gather much
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traits.hunt_preference = random.uniform(0.2, 0.5)
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traits.gather_preference = random.uniform(0.2, 0.5)
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elif archetype == "hoarder":
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traits.hoarding_rate = random.uniform(0.7, 0.95)
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traits.market_affinity = random.uniform(0.1, 0.4)
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traits.trade_preference = random.uniform(0.3, 0.7)
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elif archetype == "woodcutter":
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traits.woodcut_preference = random.uniform(1.3, 2.0)
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traits.gather_preference = random.uniform(0.5, 0.8)
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return traits
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def generate_random_skills(personality: PersonalityTraits, age: Optional[int] = None) -> Skills:
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"""Generate starting skills influenced by personality and age.
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Agents with strong preferences start with slightly better skills
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in those areas (natural talent).
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Older agents start with higher skills (life experience).
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"""
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# Base skill level with small random variation
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base = 1.0
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variance = 0.15
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# Age bonus: older agents have more experience
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age_bonus = 0.0
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if age is not None:
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# Young agents (< 25): no bonus
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# Prime agents (25-45): small bonus
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# Old agents (> 45): larger bonus (wisdom)
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if age >= 45:
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age_bonus = 0.3 + random.uniform(0, 0.2)
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elif age >= 25:
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age_bonus = (age - 25) * 0.01 + random.uniform(0, 0.1)
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skills = Skills(
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hunting=base + random.uniform(-variance, variance) + (personality.hunt_preference - 1.0) * 0.1 + age_bonus,
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gathering=base + random.uniform(-variance, variance) + (personality.gather_preference - 1.0) * 0.1 + age_bonus,
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woodcutting=base + random.uniform(-variance, variance) + (personality.woodcut_preference - 1.0) * 0.1 + age_bonus,
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trading=base + random.uniform(-variance, variance) + (personality.trade_preference - 1.0) * 0.1 + age_bonus,
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crafting=base + random.uniform(-variance, variance) + age_bonus,
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)
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# Clamp all skills to valid range
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skills.hunting = max(skills.MIN_SKILL, min(skills.MAX_SKILL, skills.hunting))
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skills.gathering = max(skills.MIN_SKILL, min(skills.MAX_SKILL, skills.gathering))
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skills.woodcutting = max(skills.MIN_SKILL, min(skills.MAX_SKILL, skills.woodcutting))
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skills.trading = max(skills.MIN_SKILL, min(skills.MAX_SKILL, skills.trading))
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skills.crafting = max(skills.MIN_SKILL, min(skills.MAX_SKILL, skills.crafting))
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return skills
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def determine_profession(personality: PersonalityTraits, skills: Skills) -> ProfessionType:
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"""Determine an agent's emergent profession based on traits and skills.
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This is for display/statistics - it doesn't affect behavior directly.
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The behavior is determined by the traits and skills themselves.
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"""
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# Calculate profession scores
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scores = {
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ProfessionType.HUNTER: personality.hunt_preference * skills.hunting * 1.2,
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ProfessionType.GATHERER: personality.gather_preference * skills.gathering,
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ProfessionType.WOODCUTTER: personality.woodcut_preference * skills.woodcutting,
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ProfessionType.TRADER: personality.trade_preference * skills.trading * personality.market_affinity * 1.5,
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}
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# Find the best match
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best_profession = max(scores, key=scores.get)
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best_score = scores[best_profession]
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# If no clear winner (all scores similar), they're a generalist
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second_best = sorted(scores.values(), reverse=True)[1]
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if best_score < second_best * 1.2:
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return ProfessionType.GENERALIST
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return best_profession
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def get_action_skill_modifier(skill_value: float) -> float:
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"""Convert skill value to action modifier.
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Skill 0.5 = 0.75x effectiveness
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Skill 1.0 = 1.0x effectiveness
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Skill 1.5 = 1.25x effectiveness
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Skill 2.0 = 1.5x effectiveness
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This creates meaningful but not overpowering differences.
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"""
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# Linear scaling: (skill - 1.0) * 0.5 + 1.0
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# So skill 0.5 -> 0.75, skill 1.0 -> 1.0, skill 2.0 -> 1.5
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return max(0.5, min(1.5, 0.5 * skill_value + 0.5))
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def get_trade_price_modifier(skill_value: float, is_buying: bool) -> float:
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"""Get price modifier for trading based on skill.
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Higher trading skill = better deals:
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- When buying: lower prices (modifier < 1)
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- When selling: higher prices (modifier > 1)
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Skill 1.0 = no modifier
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Skill 2.0 = 15% better deals
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"""
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modifier = (skill_value - 1.0) * 0.15
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if is_buying:
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return max(0.85, 1.0 - modifier) # Lower is better for buying
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else:
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return min(1.15, 1.0 + modifier) # Higher is better for selling
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