Implement OpenCog systems integration for symbolic reasoning in OpenManus-RL

[Copilot]

This PR implements a comprehensive OpenCog systems integration that adds symbolic reasoning and cognitive architecture capabilities to OpenManus-RL, addressing the need for hybrid AI that combines neural reinforcement learning with symbolic knowledge representation.

🧠 What This Adds

The integration provides five core components that work together to enable symbolic reasoning in RL agents:

AtomSpace Integration

A hypergraph-based knowledge representation system that stores facts, concepts, and relationships with truth values and confidence measures:

from openmanus_rl.opencog_systems import AtomSpaceManager

atomspace = AtomSpaceManager()
agent_concept = atomspace.create_concept_node("agent")
action_concept = atomspace.create_concept_node("explore")
can_perform = atomspace.create_predicate_node("can_perform")

# Create knowledge: agent can perform exploration with 80% confidence
atomspace.create_evaluation_link(can_perform, [agent_concept, action_concept], truth_value=0.8)

Reasoning Engine

Forward and backward chaining inference capabilities with probabilistic reasoning:

from openmanus_rl.opencog_systems import OpenCogReasoningEngine

reasoning_engine = OpenCogReasoningEngine(atomspace)

# Query: What actions can the agent perform?
goal = {"type": "evaluation", "predicate": "can_perform", "args": ["agent", "$action"]}
result = reasoning_engine.backward_chaining(goal)

print(f"Reasoning confidence: {result.confidence}")
print(f"Derived conclusions: {result.conclusion}")

Cognitive Architecture

Complete cognitive processing cycles that integrate perception, reasoning, planning, and learning:

from openmanus_rl.opencog_systems import CognitiveAgent

agent = CognitiveAgent("research_assistant")

# Complete cognitive cycle with symbolic reasoning
observations = {"environment": "laboratory", "task": "analyze_samples"}
result = agent.cognitive_cycle(observations, goal="complete analysis efficiently")

# Agent reasons symbolically about observations, plans actions, and learns from outcomes

Pattern Matching

Advanced pattern recognition with exact, fuzzy, structural, and semantic matching:

from openmanus_rl.opencog_systems import OpenCogPatternMatcher, PatternQuery

matcher = OpenCogPatternMatcher(atomspace)
query = PatternQuery({"type": "evaluation", "predicate": "located_at", "args": ["$entity", "laboratory"]})

# Find all entities located in the laboratory
matches = matcher.match_pattern(query, MatchType.FUZZY)

Knowledge Graphs

High-level semantic knowledge representation with ontologies and relationship inference:

from openmanus_rl.opencog_systems import KnowledgeGraph, RelationType

kg = KnowledgeGraph(atomspace)
lab_id = kg.add_entity("Laboratory", {"type": "location"})
agent_id = kg.add_entity("ResearchAgent", {"type": "agent"})

# Create spatial relationship
kg.add_relationship(agent_id, lab_id, RelationType.SPATIAL, 
                   properties={"relation": "located_in"})

# Find related entities and infer new knowledge
related = kg.find_related_entities(agent_id, max_distance=2)

🚀 Integration Benefits

This enables several powerful capabilities for RL agents:

Enhanced Decision Making: Agents can reason symbolically about actions and consequences, not just learn from trial and error.

Knowledge Transfer: Symbolic knowledge can be shared between agents and persists across training sessions.

Explainable AI: Reasoning paths provide clear explanations for agent decisions and actions.

Hybrid Learning: Combines the pattern recognition strengths of neural networks with the logical reasoning of symbolic AI.

🔧 Usage with Existing Systems

The integration is designed to enhance existing OpenManus-RL components without breaking changes:

# Enhanced memory with symbolic knowledge
class CognitiveMemory(SimpleMemory):
    def __init__(self):
        super().__init__()
        self.knowledge_graph = KnowledgeGraph(AtomSpaceManager())
    
    def store(self, record):
        super().store(record)
        # Also store in symbolic knowledge graph
        self.knowledge_graph.add_experience_knowledge(record)

# Enhanced reward shaping with symbolic reasoning
class SymbolicRewardShaper:
    def shape_reward(self, state, action, reward):
        # Use reasoning engine to evaluate action appropriateness
        reasoning_result = self.reasoning_engine.reason_about_action(action, state)
        return reward + (reasoning_result.confidence * 0.1)

📊 Comprehensive Implementation

The implementation includes:

• 5 core modules with 1,800+ lines of well-documented Python code
• Comprehensive test suite with 20+ unit tests covering all major functionality
• Working examples demonstrating integration with cognitive agents, knowledge graphs, and reasoning
• Detailed documentation (12,000+ words) with usage guides and API reference
• Configuration management for customizing all OpenCog parameters
• Performance optimizations with memory management and caching

🎯 Ready for Production

All components are thoroughly tested and include:

• Memory management with configurable limits
• Error handling and logging
• Performance monitoring and statistics
• Import/export capabilities for knowledge persistence
• Integration hooks for existing OpenManus-RL systems

This provides a solid foundation for researchers and practitioners who want to combine the learning capabilities of RL with the reasoning power of symbolic AI, enabling more interpretable, transferable, and robust intelligent agents.

Original prompt

implement opencog systems for openmaqnus-rl

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