🧠 Cognito Simulation Engine

For consulting and enterprise solutions: Krishna Bajpai – AI/ML & Quantum Consultant

A modular cognitive simulation engine for modeling and testing advanced AI cognitive architectures.

Cognito Simulation Engine is a groundbreaking framework designed for AGI research, providing sophisticated tools for simulating cognitive processes including symbolic reasoning, memory modeling, goal-directed behavior, and metacognitive learning agents.

🌟 Features

Core Cognitive Systems

• 🧠 Advanced Memory Modeling: Working memory, episodic memory, and long-term memory with realistic cognitive constraints
• 🎯 Goal-Directed Reasoning: Symbolic reasoning engine with forward/backward chaining and abductive inference
• 🤖 Cognitive Agents: Multiple agent architectures (Basic, Reasoning, Learning, MetaCognitive)
• 🌍 Interactive Environments: Rich environments for agent perception, action, and learning
• 📊 Comprehensive Analytics: Performance metrics, behavioral analysis, and cognitive load monitoring

Advanced Capabilities

• 🔄 Metacognitive Reflection: Agents that reason about their own cognitive processes
• 📚 Episodic Memory Simulation: Realistic memory formation, consolidation, and retrieval
• ⚡ Working Memory Constraints: Miller's 7±2 rule implementation with attention dynamics
• 🧩 Symbolic Reasoning: Rule-based inference with uncertainty handling
• 🎓 Multiple Learning Strategies: Reinforcement learning, discovery learning, and imitation learning

🚀 Quick Start

Installation

pip install cognito-sim-engine

Basic Usage

from cognito_sim_engine import CognitiveEngine, CognitiveAgent, CognitiveEnvironment
from cognito_sim_engine import Goal, Fact, SimulationConfig

# Create a cognitive environment
env = CognitiveEnvironment("Research Lab")

# Configure the simulation
config = SimulationConfig(
    max_cycles=100,
    working_memory_capacity=7,
    enable_metacognition=True,
    enable_learning=True
)

# Create the cognitive engine
engine = CognitiveEngine(config=config, environment=env)

# Create a cognitive agent
agent = CognitiveAgent("researcher_01", "Dr. Cognitive")

# Add the agent to the environment
env.add_agent("researcher_01")

# Define a research goal
research_goal = Goal(
    description="Understand the cognitive architecture",
    priority=0.8,
    target_facts=[Fact("understood", ["cognitive_architecture"])]
)

# Add goal to the agent
agent.add_goal(research_goal)

# Run the simulation
metrics = engine.run_simulation()

print(f"Simulation completed in {metrics.total_cycles} cycles")
print(f"Goals achieved: {metrics.goals_achieved}")

Command Line Interface

The package includes a powerful CLI for running simulations:

# Run a basic simulation
cogsim run --cycles 100 --agents 2 --agent-type cognitive

# Run an interactive simulation
cogsim run --interactive --cycles 50 --verbose

# Create a specialized reasoning agent
cogsim create-agent --type reasoning --name "LogicMaster"

# Run demonstration scenarios
cogsim demo --scenario reasoning --interactive

# Analyze simulation results
cogsim analyze session.json --format console

# Show system capabilities
cogsim info

🏗️ Architecture Overview

Cognitive Engine

The central orchestrator that manages cognitive cycles:

• Perception Processing: Multi-modal sensory input handling
• Memory Management: Automatic consolidation and decay
• Reasoning Coordination: Goal-directed inference execution
• Action Selection: Priority-based decision making
• Learning Integration: Experience-based adaptation

Memory System

Biologically-inspired memory architecture:

from cognito_sim_engine import MemoryManager, MemoryItem, MemoryType

# Create memory manager
memory = MemoryManager(working_capacity=7, decay_rate=0.02)

# Store different types of memories
working_memory_item = MemoryItem(
    content="Current task: analyze data",
    memory_type=MemoryType.WORKING,
    importance=0.8
)

episodic_memory_item = MemoryItem(
    content="Yesterday I learned about neural networks",
    memory_type=MemoryType.EPISODIC,
    importance=0.6
)

memory.store_memory(working_memory_item)
memory.store_memory(episodic_memory_item)

# Retrieve memories
relevant_memories = memory.search_memories("neural networks")

Reasoning Engine

Symbolic reasoning with multiple inference strategies:

from cognito_sim_engine import InferenceEngine, Rule, Fact, Goal

# Create inference engine
reasoner = InferenceEngine(depth_limit=10)

# Define reasoning rules
learning_rule = Rule(
    conditions=[
        Fact("wants_to_learn", ["?agent", "?topic"]),
        Fact("has_resource", ["?agent", "?resource"]),
        Fact("teaches", ["?resource", "?topic"])
    ],
    conclusion=Fact("should_study", ["?agent", "?resource"]),
    confidence=0.9,
    name="learning_strategy"
)

reasoner.reasoner.add_rule(learning_rule)

# Define facts
reasoner.reasoner.add_fact(Fact("wants_to_learn", ["alice", "AI"]))
reasoner.reasoner.add_fact(Fact("has_resource", ["alice", "textbook"]))
reasoner.reasoner.add_fact(Fact("teaches", ["textbook", "AI"]))

# Perform inference
goal = Goal(
    description="Learn about AI",
    target_facts=[Fact("knows", ["alice", "AI"])]
)

result = reasoner.infer(goal, list(reasoner.reasoner.facts.values()))
print(f"Reasoning successful: {result.success}")
print(f"Recommended actions: {[a.name for a in result.recommended_actions]}")

🤖 Agent Types

CognitiveAgent

Basic cognitive agent with memory, reasoning, and learning:

from cognito_sim_engine import CognitiveAgent, AgentPersonality

# Create agent with custom personality
personality = AgentPersonality(
    curiosity=0.8,      # High exploration tendency
    analyticalness=0.7, # Prefers logical reasoning
    creativity=0.6      # Moderate creative problem solving
)

agent = CognitiveAgent(
    agent_id="explorer_01",
    name="Explorer",
    personality=personality,
    working_memory_capacity=7,
    enable_metacognition=True
)

ReasoningAgent

Specialized for symbolic reasoning and logical problem solving:

from cognito_sim_engine import ReasoningAgent

reasoning_agent = ReasoningAgent("logician_01", "Dr. Logic")
# Enhanced reasoning capabilities with multiple strategies
# Automatic domain knowledge loading for problem-solving

LearningAgent

Focused on adaptive learning and skill acquisition:

from cognito_sim_engine import LearningAgent

learning_agent = LearningAgent("student_01", "Ada Learner")
# Multiple learning strategies: reinforcement, discovery, imitation
# Skill level tracking and adaptive strategy selection

MetaCognitiveAgent

Advanced agent with self-reflection and cognitive monitoring:

from cognito_sim_engine import MetaCognitiveAgent

meta_agent = MetaCognitiveAgent("philosopher_01", "Meta Thinker")
# Cognitive load monitoring
# Strategy effectiveness evaluation
# Self-model updating

🌍 Environment System

Create rich, interactive environments for agent simulation:

from cognito_sim_engine import CognitiveEnvironment, EnvironmentObject, Action

# Create environment
env = CognitiveEnvironment("Laboratory")

# Add interactive objects
microscope = EnvironmentObject(
    name="microscope",
    object_type="instrument",
    position={"x": 5, "y": 3, "z": 1},
    properties={"magnification": "1000x", "state": "available"},
    interactable=True,
    description="High-powered research microscope"
)

env.state.add_object(microscope)

# Add custom action handlers
def use_microscope(action, agent_id):
    return True  # Custom interaction logic

env.add_action_handler("use_microscope", use_microscope)

📚 Example Use Cases

1. Cognitive Architecture Research

# Study working memory limitations
config = SimulationConfig(working_memory_capacity=5)  # Below normal capacity
agent = CognitiveAgent("test_subject", working_memory_capacity=5)

# Add multiple competing goals to test cognitive load
for i in range(10):
    goal = Goal(f"Task {i}", priority=random.uniform(0.3, 0.9))
    agent.add_goal(goal)

# Monitor performance degradation
metrics = engine.run_simulation()

2. Learning Strategy Comparison

# Compare different learning approaches
reinforcement_agent = LearningAgent("rl_agent")
reinforcement_agent.learning_strategy = LearningStrategy.REINFORCEMENT

discovery_agent = LearningAgent("discovery_agent") 
discovery_agent.learning_strategy = LearningStrategy.DISCOVERY

# Run parallel simulations and compare performance

3. Metacognitive Development

# Study metacognitive development
meta_agent = MetaCognitiveAgent("developing_mind")

# Add metacognitive learning callback
def track_metacognition(agent, feedback):
    insights = len(agent.metacognitive_insights)
    print(f"Metacognitive insights: {insights}")

meta_agent.learning_callbacks.append(track_metacognition)

🔧 Configuration

Comprehensive configuration options for fine-tuning simulations:

config = SimulationConfig(
    max_cycles=1000,                    # Simulation length
    cycle_timeout=1.0,                  # Real-time cycle duration
    working_memory_capacity=7,          # Miller's magical number
    attention_threshold=0.5,            # Attention focus threshold
    goal_timeout=300.0,                 # Goal expiration time
    enable_metacognition=True,          # Metacognitive capabilities
    enable_learning=True,               # Learning mechanisms
    enable_visualization=False,         # Visual debugging
    memory_decay_rate=0.01,            # Memory decay rate
    attention_decay_rate=0.05,         # Attention decay
    reasoning_depth_limit=10,          # Maximum reasoning depth
    enable_metrics=True,               # Performance tracking
    random_seed=42                     # Reproducible results
)

📊 Analysis and Visualization

Built-in tools for analyzing cognitive behavior:

# Get comprehensive agent state
cognitive_state = agent.get_cognitive_state()

# Export simulation data
session_data = engine.export_session("simulation.json")
agent_data = agent.export_agent_data()

# Memory system analysis
memory_stats = agent.memory_manager.get_memory_statistics()
print(f"Working memory usage: {memory_stats['working_memory']['usage']:.2f}")
print(f"Total memories: {memory_stats['total_memories']}")

# Reasoning analysis
reasoning_summary = agent.inference_engine.reasoner.get_knowledge_summary()
print(f"Facts: {reasoning_summary['total_facts']}")
print(f"Rules: {reasoning_summary['total_rules']}")

🧪 Research Applications

AGI Development

• Cognitive Architecture Testing: Validate theoretical cognitive models
• Scalability Studies: Test cognitive systems under varying loads
• Integration Research: Study interaction between cognitive subsystems

Psychology & Cognitive Science

• Memory Research: Investigate memory formation and retrieval patterns
• Attention Studies: Model attention allocation and switching
• Learning Research: Compare learning strategies and effectiveness

AI Safety & Alignment

• Goal Alignment: Study how agents pursue and modify goals
• Metacognitive Safety: Research self-reflective AI behavior
• Cognitive Containment: Test cognitive limitation strategies

🛠️ Development & Extension

Plugin Architecture

# Create custom cognitive modules
from cognito_sim_engine import BaseAgent

class EmotionalAgent(BaseAgent):
    def __init__(self, agent_id, name=""):
        super().__init__(agent_id, name)
        self.emotions = {"joy": 0.5, "fear": 0.1, "anger": 0.0}
    
    def perceive(self, perceptions):
        # Custom emotional processing
        pass
    
    def reason(self):
        # Emotion-influenced reasoning
        pass

Custom Environments

# Create domain-specific environments
class SocialEnvironment(CognitiveEnvironment):
    def __init__(self):
        super().__init__("Social World")
        self.social_dynamics = SocialDynamicsEngine()
    
    def get_perceptions(self, agent_id=None):
        # Add social perceptions
        perceptions = super().get_perceptions(agent_id)
        social_perceptions = self.social_dynamics.get_social_cues(agent_id)
        return perceptions + social_perceptions

📖 Documentation

Comprehensive documentation is available at: https://krish567366.github.io/cognito-sim-engine

Documentation Sections

• Getting Started: Installation and basic usage
• Cognitive Theory: Theoretical foundations and design principles
• API Reference: Complete API documentation with examples
• Advanced Usage: Complex scenarios and customization
• Research Applications: Real-world research use cases
• Contributing: Development guidelines and contribution process

📦 Installation Options

PyPI (Recommended)

pip install cognito-sim-engine

Development Installation

git clone https://github.com/krish567366/cognito-sim-engine.git
cd cognito-sim-engine
pip install -e ".[dev,docs,visualization]"

Optional Dependencies

# For visualization capabilities
pip install cognito-sim-engine[visualization]

# For development tools
pip install cognito-sim-engine[dev]

# For documentation building
pip install cognito-sim-engine[docs]

🤝 Contributing

We welcome contributions from the AGI research community!

Areas for Contribution

• New Agent Architectures: Implement novel cognitive architectures
• Memory Models: Develop advanced memory systems
• Reasoning Engines: Create specialized reasoning capabilities
• Environment Types: Build domain-specific environments
• Analysis Tools: Develop cognitive behavior analysis tools
• Documentation: Improve documentation and tutorials

Getting Started

1. Fork the repository
2. Create a feature branch: git checkout -b feature/amazing-feature
3. Make your changes and add tests
4. Ensure all tests pass: pytest
5. Submit a pull request

Development Setup

# Clone and setup development environment
git clone https://github.com/krish567366/cognito-sim-engine.git
cd cognito-sim-engine

# Install development dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Run type checking
mypy cognito_sim_engine/

# Format code
black cognito_sim_engine/
isort cognito_sim_engine/

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

👨‍💻 Author

Krishna Bajpai

• Email: bajpaikrishna715@gmail.com
• GitHub: @krish567366

🙏 Acknowledgments

• Cognitive science research community for theoretical foundations
• Open source AI/ML community for inspiration and tools
• Beta testers and early adopters for valuable feedback

🔗 Links

• Documentation: https://krish567366.github.io/cognito-sim-engine
• PyPI Package: https://pypi.org/project/cognito-sim-engine/
• GitHub Repository: https://github.com/krish567366/cognito-sim-engine
• Issue Tracker: https://github.com/krish567366/cognito-sim-engine/issues

⭐ Support

If you find this project useful for your research, please consider:

• Starring the repository ⭐
• Citing the project in your research papers
• Contributing to the codebase
• Reporting issues and suggesting improvements

Cognito Simulation Engine - Pioneering the future of AGI research through advanced cognitive simulation.