Examples Overview¶

This section provides comprehensive examples and tutorials for using the Cognito Simulation Engine across various research scenarios, from basic agent creation to complex multi-agent simulations.

Getting Started Examples¶

Basic Agent Setup¶

The simplest way to get started with cognitive agents:

from cognito_sim_engine import CognitiveAgent, Environment, CognitiveEngine

# Create a basic cognitive agent
agent = CognitiveAgent(
    agent_id="my_first_agent",
    personality_traits={
        "openness": 0.8,
        "conscientiousness": 0.7
    }
)

# Create environment
env = Environment("basic_environment")
env.add_agent(agent)

# Create and run simulation
engine = CognitiveEngine()
engine.add_environment(env)
results = engine.run_simulation(duration=100)

print(f"Simulation completed: {results.total_steps} steps")

Memory and Learning Example¶

Demonstrating agent memory and learning capabilities:

from cognito_sim_engine import LearningAgent, MemoryType

# Create learning agent
learner = LearningAgent(
    agent_id="student_agent",
    learning_rate=0.02,
    exploration_rate=0.1
)

# Store knowledge in agent's memory
learner.store_memory(
    content="Neural networks use backpropagation for training",
    memory_type=MemoryType.SEMANTIC,
    importance=0.8,
    tags=["machine_learning", "neural_networks"]
)

# Retrieve relevant memories
memories = learner.retrieve_memories(
    query="neural network training",
    memory_types=[MemoryType.SEMANTIC],
    limit=5
)

for memory in memories:
    print(f"Retrieved: {memory.content}")

Complete Example Scenarios¶

1. Research Laboratory Simulation¶

View Full Example →

Simulate a research laboratory with multiple researchers collaborating on AI projects:

Multi-agent collaboration
Knowledge sharing and peer learning
Goal-directed research behavior
Publication and innovation tracking

2. Educational Classroom Environment¶

View Full Example →

Model an intelligent tutoring system with adaptive learning:

Teacher-student interactions
Personalized curriculum adaptation
Learning progress assessment
Collaborative learning activities

3. Competitive Learning Tournament¶

View Full Example →

Create competitions between learning agents:

Tournament-style competitions
Performance ranking systems
Strategy adaptation and evolution
Competitive learning dynamics

4. Creative Problem Solving Workshop¶

View Full Example →

Simulate creative collaboration and innovation:

Divergent and convergent thinking
Idea generation and evaluation
Cross-pollination of concepts
Innovation emergence patterns

5. Cognitive Architecture Comparison¶

View Full Example →

Compare different cognitive architectures:

Multiple reasoning strategies
Performance benchmarking
Cognitive efficiency analysis
Adaptation capability assessment

Domain-Specific Examples¶

Artificial Intelligence Research¶

AGI Development Simulation¶

from cognito_sim_engine import (
    ResearchAgent, CollaborativeEnvironment, 
    Goal, GoalType, ResearchDomain
)

# Create AGI research environment
agi_lab = CollaborativeEnvironment(
    env_id="agi_research_lab",
    collaboration_mechanisms=["joint_research", "peer_review", "knowledge_synthesis"],
    knowledge_sharing_enabled=True
)

# Create specialized research agents
researchers = []
specializations = [
    "neural_architectures",
    "reasoning_systems", 
    "memory_models",
    "learning_algorithms",
    "cognitive_architectures"
]

for i, specialty in enumerate(specializations):
    researcher = ResearchAgent(
        agent_id=f"agi_researcher_{i:03d}",
        research_domain=specialty,
        expertise_level=0.8,
        collaboration_style="open_science"
    )

    # Add research goals
    research_goal = Goal(
        goal_id=f"advance_{specialty}",
        description=f"Make breakthrough in {specialty}",
        goal_type=GoalType.ACHIEVEMENT,
        priority=0.9,
        success_criteria=[
            "novel_theoretical_contribution",
            "empirical_validation",
            "peer_recognition"
        ]
    )
    researcher.add_goal(research_goal)

    agi_lab.add_agent(researcher)
    researchers.append(researcher)

# Create interdisciplinary research project
project_goal = Goal(
    goal_id="agi_breakthrough",
    description="Develop working AGI prototype",
    goal_type=GoalType.ACHIEVEMENT,
    priority=1.0,
    collaboration_required=True,
    estimated_duration=365 * 24 * 3600  # 1 year
)

# Facilitate collaborative research
collaboration_result = agi_lab.facilitate_research_collaboration(
    research_question="How can we integrate multiple cognitive capabilities into AGI?",
    participant_agents=[r.agent_id for r in researchers]
)

print(f"AGI research collaboration: {collaboration_result.success}")

View Complete AGI Research Example →

Education Technology¶

Adaptive Learning System¶

from cognito_sim_engine import (
    LearningEnvironment, TeachingAgent, LearningAgent,
    Curriculum, AssessmentSystem
)

# Create adaptive learning curriculum
ml_curriculum = Curriculum(
    curriculum_id="machine_learning_course",
    learning_objectives=[
        "understand_supervised_learning",
        "master_neural_networks",
        "apply_deep_learning",
        "evaluate_model_performance"
    ],
    adaptive_sequencing=True,
    difficulty_scaling=True
)

# Create intelligent tutoring environment
classroom = LearningEnvironment(
    env_id="intelligent_classroom",
    curriculum=ml_curriculum,
    assessment_system="continuous_adaptive",
    personalization_enabled=True,
    collaborative_learning=True
)

# Create AI tutor
ai_tutor = TeachingAgent(
    agent_id="professor_ai",
    subject_expertise=["machine_learning", "deep_learning", "statistics"],
    teaching_strategies=["socratic", "constructivist", "adaptive"],
    personality_traits={"patience": 0.9, "enthusiasm": 0.8}
)
classroom.add_agent(ai_tutor)

# Create diverse learning agents
students = []
for i in range(25):
    student = LearningAgent(
        agent_id=f"student_{i:03d}",
        learning_rate=random.uniform(0.01, 0.05),
        learning_style=random.choice(["visual", "auditory", "kinesthetic"]),
        prior_knowledge=random.uniform(0.1, 0.4),
        motivation_level=random.uniform(0.6, 1.0)
    )
    classroom.add_agent(student)
    students.append(student)

# Simulate adaptive learning process
simulation_results = classroom.run_adaptive_learning_simulation(
    duration=12 * 7 * 24 * 3600,  # 12 weeks
    assessment_frequency=7 * 24 * 3600,  # Weekly assessments
    adaptation_triggers=["performance_drop", "engagement_low", "mastery_achieved"]
)

print(f"Learning outcomes: {simulation_results.learning_outcomes}")

View Complete Educational Simulation →

Cognitive Science Research¶

Theory of Mind Development¶

from cognito_sim_engine import (
    DevelopmentalAgent, SocialEnvironment,
    TheoryOfMindTask, CognitiveDevelopment
)

# Create developmental environment
developmental_env = SocialEnvironment(
    env_id="theory_of_mind_lab",
    social_interactions_enabled=True,
    perspective_taking_tasks=True,
    false_belief_scenarios=True
)

# Create agents with different developmental stages
agents = []
developmental_stages = ["preoperational", "concrete_operational", "formal_operational"]

for stage in developmental_stages:
    for agent_num in range(10):
        agent = DevelopmentalAgent(
            agent_id=f"{stage}_agent_{agent_num:02d}",
            developmental_stage=stage,
            theory_of_mind_level=get_tom_level_for_stage(stage),
            social_cognition_enabled=True
        )
        developmental_env.add_agent(agent)
        agents.append(agent)

# Run theory of mind development tasks
tom_tasks = [
    TheoryOfMindTask("false_belief_task", difficulty=0.6),
    TheoryOfMindTask("appearance_reality_task", difficulty=0.7),
    TheoryOfMindTask("perspective_taking_task", difficulty=0.8)
]

development_results = {}
for task in tom_tasks:
    task_results = developmental_env.administer_tom_task(
        task=task,
        participants=[agent.agent_id for agent in agents]
    )
    development_results[task.name] = task_results

# Analyze developmental patterns
development_analysis = analyze_tom_development(development_results)
print(f"Theory of Mind development patterns: {development_analysis.summary}")

View Complete Cognitive Development Example →

Advanced Usage Patterns¶

Multi-Environment Simulations¶

Running multiple connected environments:

from cognito_sim_engine import SimulationOrchestrator

# Create orchestrator for multiple environments
orchestrator = SimulationOrchestrator()

# Create connected environments
university_env = orchestrator.create_environment(
    "university_campus",
    environment_type="educational",
    capacity=1000
)

research_lab_env = orchestrator.create_environment(
    "research_laboratory", 
    environment_type="collaborative",
    capacity=50
)

industry_env = orchestrator.create_environment(
    "tech_company",
    environment_type="competitive",
    capacity=200
)

# Create agents that move between environments
mobile_agents = []
for i in range(20):
    agent = CognitiveAgent(
        agent_id=f"mobile_agent_{i:03d}",
        mobility_enabled=True,
        environment_adaptation=True
    )
    mobile_agents.append(agent)

# Set up agent migration patterns
orchestrator.configure_agent_migration(
    agents=mobile_agents,
    migration_rules={
        "university_to_research": {"trigger": "research_interest_high", "probability": 0.3},
        "research_to_industry": {"trigger": "commercialization_opportunity", "probability": 0.2},
        "industry_to_university": {"trigger": "knowledge_gaps_identified", "probability": 0.1}
    }
)

# Run multi-environment simulation
multi_env_results = orchestrator.run_simulation(
    duration=365 * 24 * 3600,  # 1 year
    cross_environment_interactions=True,
    knowledge_transfer_enabled=True
)

print(f"Multi-environment simulation: {len(multi_env_results.environment_results)} environments")

Large-Scale Simulations¶

Optimizing for large numbers of agents:

from cognito_sim_engine import ScalableSimulation, DistributedEngine

# Create scalable simulation for 10,000 agents
large_scale_sim = ScalableSimulation(
    simulation_id="large_scale_cognitive_study",
    target_agent_count=10000,
    distributed_processing=True,
    memory_optimization=True
)

# Configure distributed engine
distributed_engine = DistributedEngine(
    worker_nodes=8,
    load_balancing_strategy="cognitive_load",
    fault_tolerance=True
)

# Create agent populations
populations = {
    "researchers": 1000,
    "students": 5000, 
    "teachers": 500,
    "administrators": 100,
    "industry_partners": 400
}

# Generate agents with population-specific characteristics
for pop_type, count in populations.items():
    agents = large_scale_sim.generate_agent_population(
        population_type=pop_type,
        count=count,
        trait_distributions=get_population_traits(pop_type)
    )

# Run distributed simulation
large_scale_results = distributed_engine.run_simulation(
    simulation=large_scale_sim,
    duration=30 * 24 * 3600,  # 30 days
    checkpoint_frequency=24 * 3600,  # Daily checkpoints
    performance_monitoring=True
)

print(f"Large-scale simulation: {large_scale_results.total_agents} agents")

Code Templates and Scaffolding¶

Quick Start Templates¶

Basic Research Study Template¶

"""
Template for basic cognitive research study.
Customize the variables below for your specific research question.
"""

from cognito_sim_engine import *

# Configuration
STUDY_NAME = "my_cognitive_study"
NUM_AGENTS = 20
SIMULATION_DURATION = 3600  # 1 hour
RESEARCH_QUESTION = "How do agents learn to collaborate?"

# Create study environment
def create_study_environment():
    env = CollaborativeEnvironment(
        env_id=f"{STUDY_NAME}_environment",
        collaboration_mechanisms=["knowledge_sharing", "joint_problem_solving"],
        knowledge_sharing_enabled=True
    )
    return env

# Create study agents
def create_study_agents():
    agents = []
    for i in range(NUM_AGENTS):
        agent = CognitiveAgent(
            agent_id=f"participant_{i:03d}",
            personality_traits=generate_random_personality(),
            # Add your specific agent configurations here
        )
        agents.append(agent)
    return agents

# Run study
def run_study():
    # Setup
    env = create_study_environment()
    agents = create_study_agents()

    for agent in agents:
        env.add_agent(agent)

    # Execute simulation
    engine = CognitiveEngine()
    engine.add_environment(env)

    results = engine.run_simulation(duration=SIMULATION_DURATION)

    # Analysis
    analysis = analyze_study_results(results, RESEARCH_QUESTION)

    return results, analysis

if __name__ == "__main__":
    results, analysis = run_study()
    print(f"Study '{STUDY_NAME}' completed")
    print(f"Research insight: {analysis.main_finding}")

Educational Simulation Template¶

"""
Template for educational simulations.
Customize for different subjects and learning scenarios.
"""

from cognito_sim_engine import *

# Educational Configuration
SUBJECT = "machine_learning"
CLASS_SIZE = 25
COURSE_DURATION = 12 * 7 * 24 * 3600  # 12 weeks
LEARNING_OBJECTIVES = [
    "understand_concepts",
    "apply_knowledge", 
    "evaluate_solutions"
]

def create_educational_simulation():
    # Create curriculum
    curriculum = Curriculum(
        curriculum_id=f"{SUBJECT}_curriculum",
        learning_objectives=LEARNING_OBJECTIVES,
        adaptive_sequencing=True
    )

    # Create learning environment
    classroom = LearningEnvironment(
        env_id=f"{SUBJECT}_classroom",
        curriculum=curriculum,
        assessment_system="adaptive",
        personalization_enabled=True
    )

    # Create teacher agent
    teacher = TeachingAgent(
        agent_id="instructor",
        subject_expertise=[SUBJECT],
        teaching_strategies=["adaptive", "constructivist"]
    )
    classroom.add_agent(teacher)

    # Create student agents
    students = []
    for i in range(CLASS_SIZE):
        student = LearningAgent(
            agent_id=f"student_{i:03d}",
            learning_rate=random.uniform(0.01, 0.05),
            prior_knowledge=random.uniform(0.0, 0.3)
        )
        classroom.add_agent(student)
        students.append(student)

    return classroom, teacher, students

# Add your specific educational logic here

Best Practices and Patterns¶

Simulation Design Guidelines¶

Start Simple: Begin with basic agent configurations and gradually add complexity
Clear Objectives: Define specific research questions or educational goals
Realistic Parameters: Use empirically-grounded personality and cognitive parameters
Validation: Compare simulation results with real-world data when possible
Documentation: Maintain clear documentation of simulation assumptions and limitations

Performance Optimization¶

Memory Management: Monitor and optimize memory usage for large simulations
Computational Efficiency: Use appropriate reasoning depths and time limits
Parallel Processing: Leverage distributed computing for large-scale studies
Checkpointing: Save simulation state regularly for long-running experiments

Research Methodology¶

Experimental Design: Use proper controls and statistical analysis
Replication: Ensure simulation results are reproducible
Sensitivity Analysis: Test how results vary with parameter changes
Validation Studies: Compare with empirical data when available

These examples provide comprehensive starting points for various cognitive simulation scenarios. Each example can be customized and extended based on specific research needs and objectives.