cognito-sim-engine


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SummaryA modular cognitive simulation engine for modeling and testing advanced AI cognitive architectures
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requires_python>=3.9
licenseMIT
keywords cognitive-simulation artificial-intelligence cognitive-architecture agi symbolic-reasoning memory-modeling cognitive-agents
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            # 🧠 Cognito Simulation Engine

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**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

```bash
pip install cognito-sim-engine
```

### Basic Usage

```python
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:

```bash
# 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:

```python
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:

```python
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:

```python
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:

```python
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:

```python
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:

```python
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:

```python
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

```python
# 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

```python
# 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

```python
# 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:

```python
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:

```python
# 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

```python
# 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

```python
# 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](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)

```bash
pip install cognito-sim-engine
```

### Development Installation

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

### Optional Dependencies

```bash
# 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

```bash
# 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](LICENSE) file for details.

## 👨‍💻 Author

**Krishna Bajpai**

- Email: bajpaikrishna715@gmail.com
- GitHub: [@krish567366](https://github.com/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](https://krish567366.github.io/cognito-sim-engine)
- **PyPI Package**: [https://pypi.org/project/cognito-sim-engine/](https://pypi.org/project/cognito-sim-engine/)
- **GitHub Repository**: [https://github.com/krish567366/cognito-sim-engine](https://github.com/krish567366/cognito-sim-engine)
- **Issue Tracker**: [https://github.com/krish567366/cognito-sim-engine/issues](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.*

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    "description": "# \ud83e\udde0 Cognito Simulation Engine\r\n\r\n[![PyPI - Version](https://img.shields.io/pypi/v/cognito-sim-engine?color=green&label=PyPI&logo=pypi)](https://pypi.org/project/cognito-sim-engine/)\r\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\r\n[![Python 3.9+](https://img.shields.io/badge/python-3.9+-blue.svg)](https://www.python.org/downloads/release/python-390/)\r\n[![Documentation](https://img.shields.io/badge/docs-mkdocs-brightgreen.svg)](https://krish567366.github.io/cognito-sim-engine)\r\n[![PyPI Downloads](https://static.pepy.tech/badge/cognito-sim-engine)](https://pepy.tech/projects/cognito-sim-engine)\r\n\r\n**A modular cognitive simulation engine for modeling and testing advanced AI cognitive architectures.**\r\n\r\nCognito 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.\r\n\r\n## \ud83c\udf1f Features\r\n\r\n### Core Cognitive Systems\r\n\r\n- **\ud83e\udde0 Advanced Memory Modeling**: Working memory, episodic memory, and long-term memory with realistic cognitive constraints\r\n- **\ud83c\udfaf Goal-Directed Reasoning**: Symbolic reasoning engine with forward/backward chaining and abductive inference\r\n- **\ud83e\udd16 Cognitive Agents**: Multiple agent architectures (Basic, Reasoning, Learning, MetaCognitive)\r\n- **\ud83c\udf0d Interactive Environments**: Rich environments for agent perception, action, and learning\r\n- **\ud83d\udcca Comprehensive Analytics**: Performance metrics, behavioral analysis, and cognitive load monitoring\r\n\r\n### Advanced Capabilities\r\n\r\n- **\ud83d\udd04 Metacognitive Reflection**: Agents that reason about their own cognitive processes\r\n- **\ud83d\udcda Episodic Memory Simulation**: Realistic memory formation, consolidation, and retrieval\r\n- **\u26a1 Working Memory Constraints**: Miller's 7\u00b12 rule implementation with attention dynamics\r\n- **\ud83e\udde9 Symbolic Reasoning**: Rule-based inference with uncertainty handling\r\n- **\ud83c\udf93 Multiple Learning Strategies**: Reinforcement learning, discovery learning, and imitation learning\r\n\r\n## \ud83d\ude80 Quick Start\r\n\r\n### Installation\r\n\r\n```bash\r\npip install cognito-sim-engine\r\n```\r\n\r\n### Basic Usage\r\n\r\n```python\r\nfrom cognito_sim_engine import CognitiveEngine, CognitiveAgent, CognitiveEnvironment\r\nfrom cognito_sim_engine import Goal, Fact, SimulationConfig\r\n\r\n# Create a cognitive environment\r\nenv = CognitiveEnvironment(\"Research Lab\")\r\n\r\n# Configure the simulation\r\nconfig = SimulationConfig(\r\n    max_cycles=100,\r\n    working_memory_capacity=7,\r\n    enable_metacognition=True,\r\n    enable_learning=True\r\n)\r\n\r\n# Create the cognitive engine\r\nengine = CognitiveEngine(config=config, environment=env)\r\n\r\n# Create a cognitive agent\r\nagent = CognitiveAgent(\"researcher_01\", \"Dr. Cognitive\")\r\n\r\n# Add the agent to the environment\r\nenv.add_agent(\"researcher_01\")\r\n\r\n# Define a research goal\r\nresearch_goal = Goal(\r\n    description=\"Understand the cognitive architecture\",\r\n    priority=0.8,\r\n    target_facts=[Fact(\"understood\", [\"cognitive_architecture\"])]\r\n)\r\n\r\n# Add goal to the agent\r\nagent.add_goal(research_goal)\r\n\r\n# Run the simulation\r\nmetrics = engine.run_simulation()\r\n\r\nprint(f\"Simulation completed in {metrics.total_cycles} cycles\")\r\nprint(f\"Goals achieved: {metrics.goals_achieved}\")\r\n```\r\n\r\n### Command Line Interface\r\n\r\nThe package includes a powerful CLI for running simulations:\r\n\r\n```bash\r\n# Run a basic simulation\r\ncogsim run --cycles 100 --agents 2 --agent-type cognitive\r\n\r\n# Run an interactive simulation\r\ncogsim run --interactive --cycles 50 --verbose\r\n\r\n# Create a specialized reasoning agent\r\ncogsim create-agent --type reasoning --name \"LogicMaster\"\r\n\r\n# Run demonstration scenarios\r\ncogsim demo --scenario reasoning --interactive\r\n\r\n# Analyze simulation results\r\ncogsim analyze session.json --format console\r\n\r\n# Show system capabilities\r\ncogsim info\r\n```\r\n\r\n## \ud83c\udfd7\ufe0f Architecture Overview\r\n\r\n### Cognitive Engine\r\n\r\nThe central orchestrator that manages cognitive cycles:\r\n\r\n- **Perception Processing**: Multi-modal sensory input handling\r\n- **Memory Management**: Automatic consolidation and decay\r\n- **Reasoning Coordination**: Goal-directed inference execution\r\n- **Action Selection**: Priority-based decision making\r\n- **Learning Integration**: Experience-based adaptation\r\n\r\n### Memory System\r\n\r\nBiologically-inspired memory architecture:\r\n\r\n```python\r\nfrom cognito_sim_engine import MemoryManager, MemoryItem, MemoryType\r\n\r\n# Create memory manager\r\nmemory = MemoryManager(working_capacity=7, decay_rate=0.02)\r\n\r\n# Store different types of memories\r\nworking_memory_item = MemoryItem(\r\n    content=\"Current task: analyze data\",\r\n    memory_type=MemoryType.WORKING,\r\n    importance=0.8\r\n)\r\n\r\nepisodic_memory_item = MemoryItem(\r\n    content=\"Yesterday I learned about neural networks\",\r\n    memory_type=MemoryType.EPISODIC,\r\n    importance=0.6\r\n)\r\n\r\nmemory.store_memory(working_memory_item)\r\nmemory.store_memory(episodic_memory_item)\r\n\r\n# Retrieve memories\r\nrelevant_memories = memory.search_memories(\"neural networks\")\r\n```\r\n\r\n### Reasoning Engine\r\n\r\nSymbolic reasoning with multiple inference strategies:\r\n\r\n```python\r\nfrom cognito_sim_engine import InferenceEngine, Rule, Fact, Goal\r\n\r\n# Create inference engine\r\nreasoner = InferenceEngine(depth_limit=10)\r\n\r\n# Define reasoning rules\r\nlearning_rule = Rule(\r\n    conditions=[\r\n        Fact(\"wants_to_learn\", [\"?agent\", \"?topic\"]),\r\n        Fact(\"has_resource\", [\"?agent\", \"?resource\"]),\r\n        Fact(\"teaches\", [\"?resource\", \"?topic\"])\r\n    ],\r\n    conclusion=Fact(\"should_study\", [\"?agent\", \"?resource\"]),\r\n    confidence=0.9,\r\n    name=\"learning_strategy\"\r\n)\r\n\r\nreasoner.reasoner.add_rule(learning_rule)\r\n\r\n# Define facts\r\nreasoner.reasoner.add_fact(Fact(\"wants_to_learn\", [\"alice\", \"AI\"]))\r\nreasoner.reasoner.add_fact(Fact(\"has_resource\", [\"alice\", \"textbook\"]))\r\nreasoner.reasoner.add_fact(Fact(\"teaches\", [\"textbook\", \"AI\"]))\r\n\r\n# Perform inference\r\ngoal = Goal(\r\n    description=\"Learn about AI\",\r\n    target_facts=[Fact(\"knows\", [\"alice\", \"AI\"])]\r\n)\r\n\r\nresult = reasoner.infer(goal, list(reasoner.reasoner.facts.values()))\r\nprint(f\"Reasoning successful: {result.success}\")\r\nprint(f\"Recommended actions: {[a.name for a in result.recommended_actions]}\")\r\n```\r\n\r\n## \ud83e\udd16 Agent Types\r\n\r\n### CognitiveAgent\r\n\r\nBasic cognitive agent with memory, reasoning, and learning:\r\n\r\n```python\r\nfrom cognito_sim_engine import CognitiveAgent, AgentPersonality\r\n\r\n# Create agent with custom personality\r\npersonality = AgentPersonality(\r\n    curiosity=0.8,      # High exploration tendency\r\n    analyticalness=0.7, # Prefers logical reasoning\r\n    creativity=0.6      # Moderate creative problem solving\r\n)\r\n\r\nagent = CognitiveAgent(\r\n    agent_id=\"explorer_01\",\r\n    name=\"Explorer\",\r\n    personality=personality,\r\n    working_memory_capacity=7,\r\n    enable_metacognition=True\r\n)\r\n```\r\n\r\n### ReasoningAgent\r\n\r\nSpecialized for symbolic reasoning and logical problem solving:\r\n\r\n```python\r\nfrom cognito_sim_engine import ReasoningAgent\r\n\r\nreasoning_agent = ReasoningAgent(\"logician_01\", \"Dr. Logic\")\r\n# Enhanced reasoning capabilities with multiple strategies\r\n# Automatic domain knowledge loading for problem-solving\r\n```\r\n\r\n### LearningAgent\r\n\r\nFocused on adaptive learning and skill acquisition:\r\n\r\n```python\r\nfrom cognito_sim_engine import LearningAgent\r\n\r\nlearning_agent = LearningAgent(\"student_01\", \"Ada Learner\")\r\n# Multiple learning strategies: reinforcement, discovery, imitation\r\n# Skill level tracking and adaptive strategy selection\r\n```\r\n\r\n### MetaCognitiveAgent\r\n\r\nAdvanced agent with self-reflection and cognitive monitoring:\r\n\r\n```python\r\nfrom cognito_sim_engine import MetaCognitiveAgent\r\n\r\nmeta_agent = MetaCognitiveAgent(\"philosopher_01\", \"Meta Thinker\")\r\n# Cognitive load monitoring\r\n# Strategy effectiveness evaluation\r\n# Self-model updating\r\n```\r\n\r\n## \ud83c\udf0d Environment System\r\n\r\nCreate rich, interactive environments for agent simulation:\r\n\r\n```python\r\nfrom cognito_sim_engine import CognitiveEnvironment, EnvironmentObject, Action\r\n\r\n# Create environment\r\nenv = CognitiveEnvironment(\"Laboratory\")\r\n\r\n# Add interactive objects\r\nmicroscope = EnvironmentObject(\r\n    name=\"microscope\",\r\n    object_type=\"instrument\",\r\n    position={\"x\": 5, \"y\": 3, \"z\": 1},\r\n    properties={\"magnification\": \"1000x\", \"state\": \"available\"},\r\n    interactable=True,\r\n    description=\"High-powered research microscope\"\r\n)\r\n\r\nenv.state.add_object(microscope)\r\n\r\n# Add custom action handlers\r\ndef use_microscope(action, agent_id):\r\n    return True  # Custom interaction logic\r\n\r\nenv.add_action_handler(\"use_microscope\", use_microscope)\r\n```\r\n\r\n## \ud83d\udcda Example Use Cases\r\n\r\n### 1. Cognitive Architecture Research\r\n\r\n```python\r\n# Study working memory limitations\r\nconfig = SimulationConfig(working_memory_capacity=5)  # Below normal capacity\r\nagent = CognitiveAgent(\"test_subject\", working_memory_capacity=5)\r\n\r\n# Add multiple competing goals to test cognitive load\r\nfor i in range(10):\r\n    goal = Goal(f\"Task {i}\", priority=random.uniform(0.3, 0.9))\r\n    agent.add_goal(goal)\r\n\r\n# Monitor performance degradation\r\nmetrics = engine.run_simulation()\r\n```\r\n\r\n### 2. Learning Strategy Comparison\r\n\r\n```python\r\n# Compare different learning approaches\r\nreinforcement_agent = LearningAgent(\"rl_agent\")\r\nreinforcement_agent.learning_strategy = LearningStrategy.REINFORCEMENT\r\n\r\ndiscovery_agent = LearningAgent(\"discovery_agent\") \r\ndiscovery_agent.learning_strategy = LearningStrategy.DISCOVERY\r\n\r\n# Run parallel simulations and compare performance\r\n```\r\n\r\n### 3. Metacognitive Development\r\n\r\n```python\r\n# Study metacognitive development\r\nmeta_agent = MetaCognitiveAgent(\"developing_mind\")\r\n\r\n# Add metacognitive learning callback\r\ndef track_metacognition(agent, feedback):\r\n    insights = len(agent.metacognitive_insights)\r\n    print(f\"Metacognitive insights: {insights}\")\r\n\r\nmeta_agent.learning_callbacks.append(track_metacognition)\r\n```\r\n\r\n## \ud83d\udd27 Configuration\r\n\r\nComprehensive configuration options for fine-tuning simulations:\r\n\r\n```python\r\nconfig = SimulationConfig(\r\n    max_cycles=1000,                    # Simulation length\r\n    cycle_timeout=1.0,                  # Real-time cycle duration\r\n    working_memory_capacity=7,          # Miller's magical number\r\n    attention_threshold=0.5,            # Attention focus threshold\r\n    goal_timeout=300.0,                 # Goal expiration time\r\n    enable_metacognition=True,          # Metacognitive capabilities\r\n    enable_learning=True,               # Learning mechanisms\r\n    enable_visualization=False,         # Visual debugging\r\n    memory_decay_rate=0.01,            # Memory decay rate\r\n    attention_decay_rate=0.05,         # Attention decay\r\n    reasoning_depth_limit=10,          # Maximum reasoning depth\r\n    enable_metrics=True,               # Performance tracking\r\n    random_seed=42                     # Reproducible results\r\n)\r\n```\r\n\r\n## \ud83d\udcca Analysis and Visualization\r\n\r\nBuilt-in tools for analyzing cognitive behavior:\r\n\r\n```python\r\n# Get comprehensive agent state\r\ncognitive_state = agent.get_cognitive_state()\r\n\r\n# Export simulation data\r\nsession_data = engine.export_session(\"simulation.json\")\r\nagent_data = agent.export_agent_data()\r\n\r\n# Memory system analysis\r\nmemory_stats = agent.memory_manager.get_memory_statistics()\r\nprint(f\"Working memory usage: {memory_stats['working_memory']['usage']:.2f}\")\r\nprint(f\"Total memories: {memory_stats['total_memories']}\")\r\n\r\n# Reasoning analysis\r\nreasoning_summary = agent.inference_engine.reasoner.get_knowledge_summary()\r\nprint(f\"Facts: {reasoning_summary['total_facts']}\")\r\nprint(f\"Rules: {reasoning_summary['total_rules']}\")\r\n```\r\n\r\n## \ud83e\uddea Research Applications\r\n\r\n### AGI Development\r\n\r\n- **Cognitive Architecture Testing**: Validate theoretical cognitive models\r\n- **Scalability Studies**: Test cognitive systems under varying loads\r\n- **Integration Research**: Study interaction between cognitive subsystems\r\n\r\n### Psychology & Cognitive Science\r\n\r\n- **Memory Research**: Investigate memory formation and retrieval patterns\r\n- **Attention Studies**: Model attention allocation and switching\r\n- **Learning Research**: Compare learning strategies and effectiveness\r\n\r\n### AI Safety & Alignment\r\n\r\n- **Goal Alignment**: Study how agents pursue and modify goals\r\n- **Metacognitive Safety**: Research self-reflective AI behavior\r\n- **Cognitive Containment**: Test cognitive limitation strategies\r\n\r\n## \ud83d\udee0\ufe0f Development & Extension\r\n\r\n### Plugin Architecture\r\n\r\n```python\r\n# Create custom cognitive modules\r\nfrom cognito_sim_engine import BaseAgent\r\n\r\nclass EmotionalAgent(BaseAgent):\r\n    def __init__(self, agent_id, name=\"\"):\r\n        super().__init__(agent_id, name)\r\n        self.emotions = {\"joy\": 0.5, \"fear\": 0.1, \"anger\": 0.0}\r\n    \r\n    def perceive(self, perceptions):\r\n        # Custom emotional processing\r\n        pass\r\n    \r\n    def reason(self):\r\n        # Emotion-influenced reasoning\r\n        pass\r\n```\r\n\r\n### Custom Environments\r\n\r\n```python\r\n# Create domain-specific environments\r\nclass SocialEnvironment(CognitiveEnvironment):\r\n    def __init__(self):\r\n        super().__init__(\"Social World\")\r\n        self.social_dynamics = SocialDynamicsEngine()\r\n    \r\n    def get_perceptions(self, agent_id=None):\r\n        # Add social perceptions\r\n        perceptions = super().get_perceptions(agent_id)\r\n        social_perceptions = self.social_dynamics.get_social_cues(agent_id)\r\n        return perceptions + social_perceptions\r\n```\r\n\r\n## \ud83d\udcd6 Documentation\r\n\r\nComprehensive documentation is available at: [https://krish567366.github.io/cognito-sim-engine](https://krish567366.github.io/cognito-sim-engine)\r\n\r\n### Documentation Sections\r\n\r\n- **Getting Started**: Installation and basic usage\r\n- **Cognitive Theory**: Theoretical foundations and design principles\r\n- **API Reference**: Complete API documentation with examples\r\n- **Advanced Usage**: Complex scenarios and customization\r\n- **Research Applications**: Real-world research use cases\r\n- **Contributing**: Development guidelines and contribution process\r\n\r\n## \ud83d\udce6 Installation Options\r\n\r\n### PyPI (Recommended)\r\n\r\n```bash\r\npip install cognito-sim-engine\r\n```\r\n\r\n### Development Installation\r\n\r\n```bash\r\ngit clone https://github.com/krish567366/cognito-sim-engine.git\r\ncd cognito-sim-engine\r\npip install -e \".[dev,docs,visualization]\"\r\n```\r\n\r\n### Optional Dependencies\r\n\r\n```bash\r\n# For visualization capabilities\r\npip install cognito-sim-engine[visualization]\r\n\r\n# For development tools\r\npip install cognito-sim-engine[dev]\r\n\r\n# For documentation building\r\npip install cognito-sim-engine[docs]\r\n```\r\n\r\n## \ud83e\udd1d Contributing\r\n\r\nWe welcome contributions from the AGI research community!\r\n\r\n### Areas for Contribution\r\n\r\n- **New Agent Architectures**: Implement novel cognitive architectures\r\n- **Memory Models**: Develop advanced memory systems\r\n- **Reasoning Engines**: Create specialized reasoning capabilities\r\n- **Environment Types**: Build domain-specific environments\r\n- **Analysis Tools**: Develop cognitive behavior analysis tools\r\n- **Documentation**: Improve documentation and tutorials\r\n\r\n### Getting Started\r\n\r\n1. Fork the repository\r\n2. Create a feature branch: `git checkout -b feature/amazing-feature`\r\n3. Make your changes and add tests\r\n4. Ensure all tests pass: `pytest`\r\n5. Submit a pull request\r\n\r\n### Development Setup\r\n\r\n```bash\r\n# Clone and setup development environment\r\ngit clone https://github.com/krish567366/cognito-sim-engine.git\r\ncd cognito-sim-engine\r\n\r\n# Install development dependencies\r\npip install -e \".[dev]\"\r\n\r\n# Run tests\r\npytest\r\n\r\n# Run type checking\r\nmypy cognito_sim_engine/\r\n\r\n# Format code\r\nblack cognito_sim_engine/\r\nisort cognito_sim_engine/\r\n```\r\n\r\n## \ud83d\udcc4 License\r\n\r\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\r\n\r\n## \ud83d\udc68\u200d\ud83d\udcbb Author\r\n\r\n**Krishna Bajpai**\r\n\r\n- Email: bajpaikrishna715@gmail.com\r\n- GitHub: [@krish567366](https://github.com/krish567366)\r\n\r\n## \ud83d\ude4f Acknowledgments\r\n\r\n- Cognitive science research community for theoretical foundations\r\n- Open source AI/ML community for inspiration and tools\r\n- Beta testers and early adopters for valuable feedback\r\n\r\n## \ud83d\udd17 Links\r\n\r\n- **Documentation**: [https://krish567366.github.io/cognito-sim-engine](https://krish567366.github.io/cognito-sim-engine)\r\n- **PyPI Package**: [https://pypi.org/project/cognito-sim-engine/](https://pypi.org/project/cognito-sim-engine/)\r\n- **GitHub Repository**: [https://github.com/krish567366/cognito-sim-engine](https://github.com/krish567366/cognito-sim-engine)\r\n- **Issue Tracker**: [https://github.com/krish567366/cognito-sim-engine/issues](https://github.com/krish567366/cognito-sim-engine/issues)\r\n\r\n## \u2b50 Support\r\n\r\nIf you find this project useful for your research, please consider:\r\n\r\n- Starring the repository \u2b50\r\n- Citing the project in your research papers\r\n- Contributing to the codebase\r\n- Reporting issues and suggesting improvements\r\n\r\n---\r\n\r\n*Cognito Simulation Engine - Pioneering the future of AGI research through advanced cognitive simulation.*\r\n",
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