This PR implements a complete OpenCog cognitive architecture using multi-dimensional tensors as the foundational computational substrate, addressing the issue request to "implement opencog as a multi-dimensional tensor inference engine".

Overview

The implementation provides a tensor-based cognitive architecture that combines symbolic reasoning with neural computation, enabling efficient knowledge representation, inference, attention allocation, and learning through tensor operations.

Key Components Added

Core Architecture

• Tensor-based Atoms (opencog/atoms/): Knowledge representation with 128-dimensional embeddings and probabilistic truth values computed using tensor operations
• Inference Engine (opencog/inference/): Rule-based reasoning system with built-in deduction, induction, abduction, and revision rules using tensor propagation
• Attention Allocation (opencog/attention/): Neural attention mechanism with Short/Long/Very-Long Term Importance (STI/LTI/VLTI) and specialized attention agents
• Standalone Tensor Library (opencog/tensor_wrapper.h/c): Simple, efficient tensor operations without complex dependencies

High-Level API

The main opencog.h/c provides an easy-to-use interface for cognitive operations:

// Create system and add knowledge
OpenCog *opencog = OpenCog_new(NULL);
OCAtom *cat = OpenCog_addConcept(opencog, "cat", 0.9f, 0.8f);
OCAtom *animal = OpenCog_addConcept(opencog, "animal", 0.95f, 0.9f);

// Create relationships and run inference
OpenCog_associateAtoms(opencog, cat, animal, 0.85f);
OpenCog_forwardChain(opencog, 10);

// Attention processing and similarity computation
OpenCog_boostAttention(opencog, cat, 20.0f);
float similarity = OpenCog_similarity(opencog, cat, animal);

Technical Innovations

Neural-Symbolic Integration

• Every atom has a high-dimensional tensor embedding enabling similarity computation and neural learning
• Truth values are computed using tensor operations for uncertainty propagation
• Pattern matching uses tensor dot products and similarity metrics for efficiency

Attention Dynamics

• Sophisticated attention allocation using tensor-based competition and spreading
• Economic model with attention rent collection and resource allocation
• Hebbian learning for strengthening frequently co-activated atom associations

Inference Capabilities

• Forward and backward chaining inference with tensor-based rule application
• Built-in probabilistic reasoning with uncertainty propagation
• Pattern matching engine using tensor similarity operations
• Support for temporal reasoning and multi-modal integration

Build System and Testing

• Complete CMake integration with the existing Torch build system
• Comprehensive test suite (test_opencog.c) covering all components
• Working demo program (example_opencog_demo.c) showcasing features
• Detailed documentation with usage examples and API reference

Performance Considerations

The implementation is designed for efficiency:

• Tensor operations enable batch processing and vectorization
• Memory-efficient attention allocation with configurable thresholds
• Optimized similarity computation using dot products
• Scalable architecture supporting thousands of atoms

This provides a solid foundation for building intelligent systems that require both symbolic reasoning capabilities and neural learning, suitable for applications in natural language processing, robotics, and artificial general intelligence research.

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