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API Overview

Welcome to the Cognito Simulation Engine API documentation. This section provides comprehensive technical reference for all classes, methods, and interfaces in the simulation engine.

Core Architecture

The Cognito Simulation Engine is built with a modular architecture consisting of several key components:

Primary Modules

  • Engine - Core simulation engine and cognitive processing
  • Memory - Memory systems (working, episodic, semantic, long-term)
  • Reasoning - Inference engine and symbolic reasoning
  • Agents - Agent architectures and behaviors
  • Environment - Simulation environments and dynamics

Integration Components

  • CLI - Command-line interface utilities
  • Utils - Utility functions and helpers
  • Types - Type definitions and data structures

Quick Start Example

from cognito_sim_engine import CognitiveEngine, CognitiveAgent, Environment

# Create simulation environment
env = Environment("research_lab", environment_type="collaborative")

# Create cognitive agent
agent = CognitiveAgent(
    agent_id="researcher_001",
    personality_traits={
        "openness": 0.8,
        "conscientiousness": 0.9,
        "extraversion": 0.6
    }
)

# Create simulation engine
engine = CognitiveEngine()
engine.add_environment(env)
env.add_agent(agent)

# Run simulation
results = engine.run_simulation(duration=3600)

Core Classes Reference

CognitiveEngine

The main simulation engine that orchestrates all cognitive processes.

class CognitiveEngine:
    def __init__(self, config: Optional[Dict] = None)
    def add_environment(self, environment: Environment) -> None
    def run_simulation(self, duration: int, **kwargs) -> SimulationResults
    def step(self) -> StepResults
    def get_state(self) -> EngineState

Key Methods:

  • run_simulation() - Execute simulation for specified duration
  • step() - Execute single simulation step
  • add_environment() - Add environment to simulation
  • get_metrics() - Retrieve simulation metrics

CognitiveAgent

Base class for all cognitive agents with memory, reasoning, and goal management.

class CognitiveAgent:
    def __init__(self, agent_id: str, **kwargs)
    def perceive(self, environment: Environment) -> Perception
    def reason(self, perception: Perception) -> ReasoningResult
    def act(self, reasoning_result: ReasoningResult) -> Action
    def learn(self, experience: Experience) -> None

Key Properties:

  • memory_manager - Access to agent's memory systems
  • reasoning_engine - Inference and reasoning capabilities
  • goal_manager - Goal setting and achievement tracking
  • personality_traits - Personality configuration

Memory Systems

WorkingMemory

Short-term memory for active information processing.

class WorkingMemory:
    def __init__(self, capacity: int = 7, decay_rate: float = 0.1)
    def store(self, item: MemoryItem, activation: float = 1.0) -> None
    def retrieve(self, query: str, limit: int = None) -> List[MemoryItem]
    def update_activations(self) -> None

EpisodicMemory

Memory for personal experiences and events.

class EpisodicMemory:
    def __init__(self, capacity: int = 10000)
    def store_episode(self, episode: Episode) -> None
    def retrieve_episodes(self, query: str, **kwargs) -> List[Episode]
    def find_similar_episodes(self, target: Episode, threshold: float = 0.7) -> List[Episode]

SemanticMemory

Memory for general knowledge and concepts.

class SemanticMemory:
    def __init__(self)
    def store_concept(self, concept: Concept) -> None
    def retrieve_concepts(self, query: str, **kwargs) -> List[Concept]
    def get_related_concepts(self, concept_id: str, relation_type: str = None) -> List[Concept]

Reasoning Components

InferenceEngine

Core reasoning and inference capabilities.

class InferenceEngine:
    def __init__(self, config: Optional[Dict] = None)
    def infer(self, facts: List[Fact], rules: List[Rule], goal: Optional[Goal] = None) -> InferenceResult
    def forward_chain(self, facts: List[Fact], rules: List[Rule]) -> List[Fact]
    def backward_chain(self, goal: Goal, facts: List[Fact], rules: List[Rule]) -> bool

SymbolicReasoner

Symbolic logic and rule-based reasoning.

class SymbolicReasoner:
    def __init__(self, depth_limit: int = 10, breadth_limit: int = 100)
    def reason(self, premises: List[Premise], conclusion: Conclusion) -> ReasoningResult
    def validate_reasoning(self, reasoning_chain: List[ReasoningStep]) -> bool

Environment System

Environment

Base environment class for all simulation environments.

class Environment:
    def __init__(self, env_id: str, environment_type: str = "basic", **kwargs)
    def add_agent(self, agent: CognitiveAgent) -> None
    def remove_agent(self, agent_id: str) -> None
    def step(self) -> EnvironmentState
    def get_percepts_for_agent(self, agent_id: str) -> List[Percept]

Specialized Environments:

  • CollaborativeEnvironment - Multi-agent collaboration
  • LearningEnvironment - Educational simulations
  • CompetitiveEnvironment - Competition scenarios

Data Types and Structures

Core Data Types

# Basic simulation types
@dataclass
class Perception:
    agent_id: str
    timestamp: float
    sensory_data: Dict[str, Any]
    environmental_state: EnvironmentState

@dataclass
class Action:
    agent_id: str
    action_type: str
    parameters: Dict[str, Any]
    timestamp: float

@dataclass
class Experience:
    agent_id: str
    perception: Perception
    action: Action
    outcome: Any
    reward: float
    timestamp: float

# Memory types
@dataclass
class MemoryItem:
    content: Any
    activation: float
    timestamp: float
    memory_type: MemoryType
    tags: List[str]

@dataclass
class Episode:
    episode_id: str
    agent_id: str
    timestamp: float
    events: List[Event]
    context: Dict[str, Any]
    emotional_state: EmotionalState

# Reasoning types
@dataclass
class Fact:
    predicate: str
    arguments: List[str]
    confidence: float
    source: str

@dataclass
class Rule:
    rule_id: str
    conditions: List[Fact]
    conclusion: Fact
    confidence: float

@dataclass
class Goal:
    goal_id: str
    description: str
    goal_type: GoalType
    priority: float
    success_criteria: List[str]
    deadline: Optional[datetime]

Enumerations

class AgentType(Enum):
    COGNITIVE = "cognitive"
    LEARNING = "learning"
    REACTIVE = "reactive"
    DELIBERATIVE = "deliberative"

class MemoryType(Enum):
    WORKING = "working"
    EPISODIC = "episodic"
    SEMANTIC = "semantic"
    LONG_TERM = "long_term"

class GoalType(Enum):
    ACHIEVEMENT = "achievement"
    MAINTENANCE = "maintenance"
    AVOIDANCE = "avoidance"

class EnvironmentType(Enum):
    BASIC = "basic"
    COLLABORATIVE = "collaborative"
    COMPETITIVE = "competitive"
    LEARNING = "learning"

Configuration Objects

Engine Configuration

@dataclass
class EngineConfig:
    # Simulation parameters
    time_step: float = 1.0
    max_steps: int = 1000
    real_time_factor: float = 1.0

    # Processing parameters
    parallel_processing: bool = False
    max_threads: int = 4
    batch_size: int = 100

    # Memory management
    memory_cleanup_interval: int = 100
    max_memory_usage: int = 1000000

    # Logging and debugging
    log_level: str = "INFO"
    debug_mode: bool = False
    profile_performance: bool = False

Agent Configuration

@dataclass
class AgentConfig:
    # Agent parameters
    agent_type: AgentType = AgentType.COGNITIVE
    personality_traits: Dict[str, float] = None

    # Memory configuration
    working_memory_capacity: int = 7
    episodic_memory_capacity: int = 10000
    semantic_memory_enabled: bool = True

    # Reasoning configuration
    reasoning_depth: int = 5
    confidence_threshold: float = 0.6
    inference_timeout: float = 5.0

    # Learning configuration
    learning_rate: float = 0.01
    exploration_rate: float = 0.1
    adaptation_enabled: bool = True

Error Handling

Custom Exceptions

class CognitoSimError(Exception):
    """Base exception for Cognito Simulation Engine"""
    pass

class AgentError(CognitoSimError):
    """Exceptions related to agent operations"""
    pass

class MemoryError(CognitoSimError):
    """Exceptions related to memory operations"""
    pass

class ReasoningError(CognitoSimError):
    """Exceptions related to reasoning operations"""
    pass

class EnvironmentError(CognitoSimError):
    """Exceptions related to environment operations"""
    pass

class SimulationError(CognitoSimError):
    """Exceptions related to simulation execution"""
    pass

Error Handling Patterns

try:
    # Agent operations
    agent = CognitiveAgent("agent_001")
    result = agent.reason(perception)
except AgentError as e:
    logger.error(f"Agent error: {e}")
    # Handle agent-specific errors

try:
    # Memory operations
    memory_item = agent.memory_manager.retrieve("query")
except MemoryError as e:
    logger.error(f"Memory error: {e}")
    # Handle memory-specific errors

try:
    # Simulation operations
    engine = CognitiveEngine()
    results = engine.run_simulation(duration=3600)
except SimulationError as e:
    logger.error(f"Simulation error: {e}")
    # Handle simulation-specific errors

Performance Considerations

Memory Management

# Efficient memory usage patterns
agent_config = AgentConfig(
    working_memory_capacity=7,  # Realistic cognitive limits
    episodic_memory_capacity=5000,  # Reasonable for long simulations
    memory_cleanup_enabled=True  # Automatic cleanup
)

# Memory monitoring
memory_stats = agent.memory_manager.get_memory_statistics()
if memory_stats.usage_percentage > 0.8:
    agent.memory_manager.cleanup_old_memories()

Reasoning Optimization

# Efficient reasoning configuration
reasoning_config = {
    "max_depth": 10,  # Prevent infinite recursion
    "timeout": 5.0,   # Reasonable time limits
    "cache_enabled": True,  # Cache reasoning results
    "parallel_processing": False  # For thread safety
}

inference_engine = InferenceEngine(reasoning_config)

Simulation Scaling

# Large-scale simulation patterns
engine_config = EngineConfig(
    parallel_processing=True,
    max_threads=4,
    batch_size=50,
    memory_cleanup_interval=100
)

# Monitor performance
engine = CognitiveEngine(engine_config)
engine.enable_performance_monitoring()
results = engine.run_simulation(duration=3600)
performance_metrics = engine.get_performance_metrics()

Integration Patterns

Custom Agent Types

class ResearchAgent(CognitiveAgent):
    def __init__(self, agent_id: str, research_domain: str, **kwargs):
        super().__init__(agent_id, **kwargs)
        self.research_domain = research_domain
        self.research_history = []

    def conduct_research(self, research_question: str) -> ResearchResult:
        # Custom research behavior
        perception = self.perceive_research_context(research_question)
        reasoning_result = self.reason_about_research(perception)
        research_action = self.plan_research_action(reasoning_result)
        return self.execute_research(research_action)

Custom Environments

class AcademicEnvironment(Environment):
    def __init__(self, env_id: str, **kwargs):
        super().__init__(env_id, environment_type="academic", **kwargs)
        self.research_papers = []
        self.collaboration_network = CollaborationNetwork()

    def facilitate_research_collaboration(self, agents: List[CognitiveAgent]) -> None:
        # Custom collaboration logic
        for agent in agents:
            collaborators = self.find_potential_collaborators(agent)
            self.initiate_collaboration(agent, collaborators)

Event Handling

# Custom event handlers
def on_agent_learning(agent: CognitiveAgent, learning_event: LearningEvent):
    logger.info(f"Agent {agent.agent_id} learned: {learning_event.content}")

def on_goal_achieved(agent: CognitiveAgent, goal: Goal):
    logger.info(f"Agent {agent.agent_id} achieved goal: {goal.description}")

# Register event handlers
engine.register_event_handler("agent_learning", on_agent_learning)
engine.register_event_handler("goal_achieved", on_goal_achieved)

Testing and Validation

Unit Testing Patterns

import unittest
from cognito_sim_engine import CognitiveAgent, WorkingMemory

class TestWorkingMemory(unittest.TestCase):
    def setUp(self):
        self.memory = WorkingMemory(capacity=7)

    def test_memory_storage(self):
        item = MemoryItem("test_content", activation=1.0)
        self.memory.store(item)
        self.assertEqual(len(self.memory.items), 1)

    def test_memory_retrieval(self):
        item = MemoryItem("test_content", activation=1.0)
        self.memory.store(item)
        retrieved = self.memory.retrieve("test")
        self.assertGreater(len(retrieved), 0)

Integration Testing

def test_agent_environment_interaction():
    # Create test environment
    env = Environment("test_env")

    # Create test agent
    agent = CognitiveAgent("test_agent")
    env.add_agent(agent)

    # Test interaction
    perception = agent.perceive(env)
    assert perception is not None

    reasoning_result = agent.reason(perception)
    assert reasoning_result.success

    action = agent.act(reasoning_result)
    assert action.action_type is not None

This API overview provides the foundation for understanding and using the Cognito Simulation Engine. For detailed documentation on specific components, explore the individual module references.

Next Steps: