Implement AGI system with symbolic reasoning, emotional intelligence, and autonomous agents

[Copilot]

Implements a multi-paradigm AGI system integrating symbolic AI (logic inference, knowledge graphs, rule engines), emotional intelligence (emotion recognition, empathy modeling), and autonomous agent capabilities with RAG-enhanced knowledge management.

Architecture

Agent Controller

• AutoGPT-inspired goal decomposition with priority-based execution
• Dependency-aware task scheduling across multiple strategies (sequential, parallel)

Reasoning Engines

• Symbolic: Forward/backward chaining, NetworkX knowledge graphs, SymPy integration
• Emotional: VAD affective modeling (8 emotion types), context-aware empathy, emotion-influenced decisions

Knowledge Layer

• Hybrid retrieval: 768-dim vector search + triple-store graph traversal
• Memory systems: Short-term (10), long-term (1000), episodic (100) with auto-consolidation

Learning Module

• Online learning with pattern recognition and adaptive learning rates
• Q-learning with experience replay
• Transfer learning with task similarity detection
• Meta-learning for strategy optimization

Multi-Language Bridge

• Python: Core AI/ML, FastAPI REST endpoints
• Java: Apache Jena (RDF/OWL), Drools (rules), gRPC server

Usage

from agi_system import create_agi_system

agi = create_agi_system()

# Process with emotional reasoning
result = agi.process_input("I'm frustrated with this bug")
# Returns: emotion analysis, empathetic response, knowledge context

# Symbolic inference
agi.symbolic_engine.logic_system.add_fact("All birds fly")
agi.symbolic_engine.logic_system.add_rule(
    premises=["All birds fly", "Eagle is a bird"],
    conclusion="Eagle can fly"
)
proven = agi.symbolic_engine.logic_system.backward_chaining("Eagle can fly")

# Autonomous goal execution
goal_id = agi.set_goal("Research AI safety", priority="high")
result = agi.execute_goal(goal_id)  # Auto-decomposes and executes

# Hybrid knowledge query
results = agi.query_knowledge("neural networks", method="hybrid")
# Combines vector similarity + graph traversal

API

15 REST endpoints via FastAPI: /process, /goal/{set,execute}, /knowledge/{add,query}, /reason, /memory/consolidate

Security

Updated dependencies to patch DoS vulnerabilities:

• protobuf-java: 3.25.1 → 3.25.5
• gRPC: 1.60.0 → 1.75.0
• protobuf (Python): 4.25.0 → 4.28.2

Testing

29 unit/integration tests covering symbolic reasoning, emotional intelligence, and system integration.

Original prompt

Create a Powerful Agentic AGI System with Symbolic and Emotional Reasoning

Objective

Develop a comprehensive AGI (Artificial General Intelligence) system that exhibits human-like cognitive abilities, capable of understanding, learning, and applying knowledge across a wide range of domains. The system should integrate both symbolic reasoning and emotional reasoning capabilities.

Core Requirements

1. Symbolic Reasoning Engine

• Implement a symbolic AI reasoning system that can:
  • Perform logical inference and deduction
  • Handle knowledge representation using knowledge graphs
  • Execute rule-based reasoning
  • Support formal logic operations (first-order logic, propositional logic)
  • Integrate with symbolic AI libraries (e.g., PyKE, SymPy, or AIMA Python)

2. Emotional Reasoning Module

• Build an emotional intelligence system that can:
  • Recognize and classify emotions from text and context
  • Model emotional states and transitions
  • Apply affective computing principles
  • Understand sentiment and emotional context in decision-making
  • Simulate empathy and emotional responses
  • Use emotion as a factor in reasoning and planning

3. Agent Architecture (LangChain + AutoGPT Integration)

• Implement a powerful agentic system using:
  • LangChain for:
    • Chain-of-thought reasoning
    • Memory management (short-term and long-term)
    • Tool integration and orchestration
    • Prompt engineering and optimization
  • AutoGPT capabilities for:
    • Autonomous goal setting and task decomposition
    • Self-directed planning and execution
    • Iterative problem-solving
    • Multi-step reasoning and action loops

4. Multi-Language Implementation (Python & Java)

• Python components for:
  • LangChain integration
  • Machine learning models
  • Symbolic reasoning engines
  • Emotional reasoning modules
  • RAG (Retrieval-Augmented Generation) pipeline
• Java components for:
  • High-performance processing
  • Enterprise-level integration
  • Symbolic reasoning using Java-based libraries (e.g., Apache Jena for knowledge graphs)
  • Interoperability layer with Python services

5. Key Cognitive Abilities

Learning Capabilities

• Continual learning from interactions
• Transfer learning across domains
• Meta-learning (learning how to learn)
• Few-shot and zero-shot learning

Reasoning Capabilities

• Causal reasoning
• Analogical reasoning
• Abductive reasoning
• Counterfactual reasoning
• Multi-modal reasoning (combining symbolic and neural approaches)

Knowledge Management

• Dynamic knowledge graph construction and updates
• Integrate with existing RAG7 system
• Cross-domain knowledge transfer
• Context-aware knowledge retrieval

6. Architecture Components

AGI System Architecture:
├── Core Agent Controller (AutoGPT-based)
│   ├── Goal Management
│   ├── Task Decomposition
│   └── Execution Loop
├── Reasoning Layer
│   ├── Symbolic Reasoning Engine
│   │   ├── Logic Inference System
│   │   ├── Knowledge Graph Reasoner
│   │   └── Rule Engine
│   └── Emotional Reasoning Engine
│       ├── Emotion Recognition
│       ├── Affective State Modeling
│       └── Empathy Simulator
├── LangChain Integration Layer
│   ├── Chain Orchestrator
│   ├── Memory Systems
│   │   ├── Short-term Memory
│   │   ├── Long-term Memory
│   │   └── Episodic Memory
│   ├── Tool Integration
│   └── Prompt Management
├── Knowledge Layer (RAG Integration)
│   ├── Vector Database
│   ├── Knowledge Graph
│   └── Retrieval Engine
├── Learning Module
│   ├── Online Learning
│   ├── Reinforcement Learning
│   └── Transfer Learning
└── Multi-Language Bridge
    ├── Python Services
    └── Java Services

7. Technical Implementation Details

Python Requirements

• LangChain for agent orchestration
• AutoGPT components for autonomous behavior
• Symbolic AI libraries: PyKE, SymPy, AIMA-Python, or pyDatalog
• Emotional AI: TextBlob, NLTK for sentiment analysis, or custom models
• Knowledge graphs: NetworkX, RDFLib, or py2neo
• Vector databases: ChromaDB, FAISS, or Pinecone
• ML frameworks: PyTorch or TensorFlow for neural components

Java Requirements

• Apache Jena for RDF and knowledge graph management
• Drools or CLIPS for rule-based reasoning
• Spring Boot for service architecture
• JNI or gRPC for Python-Java interoperability
• Java-based symbolic reasoning libraries

8. Functional Requirements

1. Multi-Domain Understanding: Process and reason about diverse topics (science, mathematics, language, social situations, etc.)
2. Contextual Awareness: Maintain context across conversations and tasks
3. Goal-Oriented Behavior: Set and pursue goals autonomously
4. Adaptive Learning: Improve performance through experience
5. Explainable Reasoning: Provide clear explanations for decisions and conclusions
6. Emotional Intelligence: Factor in emotional context when interacting and making decisions
7. Creative Problem Solving: Gen...

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