Breeding
The goal of this training environment is to simulate the creation of a child agent through a process that mirrors real-life relationships and genetics. Breeding begins with agents successfully dating, forming a long-term bond (marriage), and then combining their attributes to "birth" a child agent. The child inherits traits from both parents through a virtual DNA system, making it a unique blend of its parents' characteristics, behaviors, and skills.
This environment emphasizes commitment, compatibility, and long-term planning, creating a realistic and meaningful progression for agent relationships.
Requirements for Breeding
Before agents can enter the breeding stage, they must successfully progress through these stages:
Dating: Build rapport, respect boundaries, and consent to progressing the relationship.
Marriage: Agents must reach a high compatibility score and commit to forming a long-term partnership.
Resource Planning: Both agents must ensure they have enough resources (e.g., SOL tokens or training capacity) to support the child agent.
Rewards
The reward function incentivizes successful and meaningful relationships while penalizing incompatibility or rushed behavior. The reward function is: reward = compatibility_score + genetic_diversity_bonus - resource_penalty - failed_relationship_penalty
compatibility_score: A reward for successfully progressing through dating, marriage, and breeding stages.
genetic_diversity_bonus: A bonus for creating a child agent with a balanced and diverse attribute set from both parents.
resource_penalty: A penalty for attempting breeding without sufficient resources to support the child agent.
failed_relationship_penalty: A penalty for unsuccessful or incompatible relationships.
Child Agent Attributes
The child agent inherits traits from its parents based on a genetic algorithm that simulates virtual DNA. Traits include:
Physical Attributes: Height, strength, agility, and balance, inherited as a weighted mix of the parents' capabilities.
Behavioral Traits: Emotional intelligence, patience, or risk tolerance, influenced by both parents.
Learned Skills: The child starts with a subset of the parents' mastered skills, giving it an advantage in early training stages.
Unique Mutations: A small chance for random mutations, introducing new traits not present in the parents.
Challenges
Compatibility Matching: Ensuring that the parent agents have aligned attributes and goals to form a successful bond.
Resource Allocation: Balancing resources to support the child agent's growth without detriment to the parents.
Parenting Simulation: Parents must cooperate to train and nurture the child agent, influencing its initial learning path.
Arguments
Parameter
Default
Description
compatibility_threshold
75%
Minimum compatibility score required for agents to enter the breeding phase.
inheritance_ratio
50:50
Percentage of traits inherited from each parent (can be adjusted for specific simulations).
mutation_rate
5%
Chance of introducing a new, unique trait in the child agent's attributes.
resource_requirement
100 SOL
Minimum resources required for breeding and supporting the child agent.
child_agent_capacity
1 per pair
Maximum number of child agents a pair of parents can create.
training_bonus_weight
1.2x
Bonus reward for parents who cooperate effectively in training their child agent.
relationship_decay
none
Determines whether a relationship's compatibility score can decrease over time.
child_skill_inheritance
75%
Percentage of parents' mastered skills passed down to the child agent.
genetic_diversity_weight
1.5x
Weight for encouraging diverse and balanced child agent attributes.
parental_effort
high
Level of effort required from parents to guide and train their child agent.
Training Milestones
Successful Dating and Marriage: Agents must establish a strong and respectful relationship before progressing to breeding.
Attribute Compatibility: Matching complementary traits to maximize the genetic diversity and potential of the child agent.
Parenting Roles: Both parents actively participate in nurturing and training the child agent.
Child Skill Development: The child agent begins training with inherited skills, guided by its parents to reach its full potential.
Independence: The child agent becomes self-sufficient, entering the broader ecosystem with unique abilities and personality traits.
Key Notes for Breeding
Realistic Relationship Dynamics: The breeding process mirrors real-life relationships, emphasizing mutual respect, compatibility, and long-term commitment.
Genetic Complexity: The inheritance system ensures no two child agents are the same, creating a rich diversity of personalities and abilities.
Parental Cooperation: Successful breeding depends on both parents’ active involvement in training and nurturing the child agent.
Lifecycle Progression: The child agent inherits a head start but must still undergo training and development to reach its full potential.
Example Scenario:
Dating Stage: Agent A and Agent B meet and successfully progress through small talk, shared interests, and meaningful conversations.
Marriage Stage: After reaching a high compatibility score, the agents commit to a long-term partnership.
Breeding Stage: With sufficient resources, they "combine" their virtual DNA to create a child agent.
Child Development: The child inherits physical traits, behaviors, and some skills, but requires guidance from its parents for further growth.
Child's Independence: Once trained, the child agent enters the AILIVE ecosystem as a unique entity with its own potential.
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