Our Team

👥 Our Team

The Symbolic AGI Toolkit (Fuzzy Symbolic Automata for AGI Graph Reasoning) is being developed by Accelflare, a research-driven Data Science and AI company specializing in high performance computing, symbolic reasoning, geospatial intelligence, and scalable systems.

We operate at the intersection of cognitive science and engineering. With a strong track record in HPC & AI research and product deployment, we blend innovation with execution to advance next-generation intelligent systems.

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Project details

Symbolic AGI Toolkit

Fuzzy Symbolic Automata for AGI Graph Reasoning 

A Symbolic Reasoning Toolkit for AGI: Fuzzy Graphs, FSMs, and Regex Integration for MeTTa and OpenCog Hyperon

🔍 Overview

The path toward Artificial General Intelligence (AGI) requires the convergence of structured knowledge representation, probabilistic reasoning, and symbolic abstraction. This proposal introduces a scalable, interpretable, and modular system for enhancing symbolic reasoning in AGI systems through the use of Fuzzy Probabilistic Bidirectional Graphs (FPBGs), Finite State Machines (FSMs), and Regular Expressions (Regex).

Built for integration with OpenCog Hyperon, particularly the MeTTa language and MORK knowledge graph, this system equips AGI frameworks with the ability to:

• Construct semantic graphs from uncertain or complex data,

• Abstract symbolic logic paths as FSMs and compressible Regex,

• Match, compare, and reason about symbolic scenes across domains.

🔗 Problem Statement

Symbolic reasoning in AGI remains constrained by:

• Limited support for fuzzy or probabilistic logic in graph models,

• Lack of symbolic abstraction pipelines for pattern generalization,

• Poor interoperability with MeTTa-based reasoning engines.

AGI agents need not only to access knowledge but to traverse and infer over it symbolically. Our toolkit addresses this by turning reasoning sequences into traversable, symbolic automata—empowering pattern discovery, comprehending and reasoning.

🛠️ System Components

1. Fuzzy Probabilistic Bidirectional Graphs (FPBGs)

   • Encodes uncertain, hierarchical, or relational knowledge.

   • Supports both similarity scoring (via [0,1]) and conflict modeling (via {-1,0,1}).

   • Operates as a symbolic intermediary between raw data and FSM abstraction.

2. Finite State Machine Compiler

   • Converts traversals in FPBGs into FSMs with annotated transitions.

   • Models reasoning sequences, temporal transformations, or data flows.

   • Enables easy visualization and testing of symbolic logic chains.

3. Regex Generator and Matcher

   • Transforms FSM paths into symbolic Regex for compact pattern encoding.

   • Matches symbolic sequences using fuzzy-weighted and optional logic.

   • Allows analog discovery and symbolic search across knowledge spaces.

4. AGI Integration Layer

   • Converts FSMs and Regex into MeTTa expressions.

   • Interfaces with MORK for graph compatibility.

   • Supports two-way interaction with AGI agents using symbolic constructs.

🌐 Enhanced Feature: Scalable Graph Streaming and Parallel Regex Matching with Accelflare AUTOMATA

To further support AGI-scale reasoning and real-time symbolic interpretation of massive knowledge structures, this proposal will be extending its architecture with a scalable symbolic graph streaming and parallel regex matching layer.

We will be also introducing a methodology to handle large graphs (>10M nodes/edges) by converting them into symbolic graph streams, partitioning them into FSM fragments, and compiling each fragment into fuzzy regular expressions. These expressions are then distributed across a parallel regex processing architecture, enabling ultra-fast symbolic scene matching.

Key benefits include:

• Edge-by-edge graph stream ingestion from large-scale data sources.
• Semantic-aware graph partitioning using community detection or motif analysis.
• FSM fragment generation with fuzzy transitions and optionality.
• Conversion to Regex rulesets and deployment across high-performance multithread and/or FPGA/ASIC based regex engines.
• Parallel evaluation and aggregation for symbolic scene recognition and analog discovery.

This crucial feature will be enabling real-time symbolic matching and graph compression for AGI agents, ensuring scalability, modularity, and fast cognitive pattern alignment within symbolic and probabilistic environments.

For more information regarding this enhancing feature please visit: Accelflare | Data Science and Analytics | AUTOMATA

🧪 Technical Innovations

• Symbolic compression of knowledge graphs using FSM-to-Regex abstraction.

• Probabilistic scene similarity through fuzzy regular expression matching.

• Symbolic learning loop: FSM paths can be reversed, reused, or refined in MeTTa.

• Plug-and-play integration with Hyperon-based systems via MeTTa interpreters.

📘 Use Cases

• Geospatial AGI: Discover symbolic landform patterns, match seasonal changes, and reason over spatial transitions.

• Industrial Robotics: Represent automata-like state transitions in manufacturing tasks symbolically and compare across sequences.

• Cognitive Planning: Build symbolic templates of prior decisions for reuse in future AGI behavior chains.

• Scene Understanding: Encode abstract symbolic representations of visual or textual data, enabling analogy-based navigation of concept graphs.

• Time Series in Financial Prediction : Convert multivariate financial time series into symbolic graphs. Identify repeating economic cycles, trend reversals, and symbolic market states using FSMs and Regex to detect patterns or anomalies in trading, sentiment, or volatility.

• Earthquake Event Prediction and Monitoring: Model seismic activity sequences as symbolic transitions. Use symbolic abstraction to compare tectonic patterns across regions, detect foreshocks or aftershock analogs, and provide real-time pattern matching for anomaly detection.

🔄 Reusability and Generalization

While the system is prototyped in geospatial reasoning and time series prediction, the abstraction model supports any AGI context where:

• Reasoning can be reduced to state transitions,

• Symbolic generalization of patterns is desirable,

• Cognitive interpretability is essential.

🤖 AGI Platform Compatibility

The project is going to be engineered with:

• Full compatibility with MeTTa: Outputs FSMs and Regex in MeTTa syntax.

• Hyperon alignment: Uses Atomspace-like constructs and non-deterministic pattern exploration.

• MORK bridge support: Converts FPBGs into MORK ingestible graphs with weighted relations and vice versa (MORK to FPBGs to FSMs to Regex Ruleset).

📈 Deliverables

• Python/MATLAB/MeTTa hybrid toolchain for FPBG → FSM → Regex

• Regex similarity engine with fuzzy pattern matcher

• Visual graph construction and transition renderer

• MeTTa expression library of symbolic patterns

• Evaluation datasets with symbolic and AGI performance metrics

⏱ Timeline and Milestones (6 Months)

• M1: FPBG + FSM Generator Prototype

• M2: Parallel Fuzzy Regex Engine & Matcher

• M3: MeTTa/MORK Compatibility Layer

• M4: Multi-domain Demonstration (Geospatial, Multivariate Time Series Prediction)

• M5: Final Documentation and Open Access Deployment

💰 Budget Overview ($150,000)

• Personnel (R&D, Integration): $85,000

• Tooling & Infrastructure: $35,000

• Evaluation & Testing: $25,000

• Documentation & Dissemination: $5,000

🎯 Evaluation Metrics

• High symbolic matching accuracy across FSM-to-Regex transformations

• Effective compression of FSM logic into concise, reusable Regex patterns

• Reliable analog pattern discovery across diverse data domains (e.g., finance, geospatial, seismic)

• Accurate symbolic forecasting and anomaly detection in time series applications

• Seamless compatibility with MeTTa language and MORK graph schemas

• Real-time performance in graph streaming and Parallel Regex pattern matching

• Scalable support for large symbolic rulebases in parallel execution environments

• Robust traceability and replay of symbolic reasoning paths

• Clear, well-documented interfaces for AGI integration and developer adoption

🌐 Broader Impact

This project enables the interpretability of AGI decision-making, allows for human-AI collaborative reasoning, and supports the reusability of learned knowledge in symbolic form. It helps pave the way for transparent AGI cognition—where reasoning is explainable, symbolic, and self-improving.

Open Source Licensing

# LICENSE - Accelflare.com

This project is multi-licensed:

## Open Source (Apache 2.0)
Core symbolic reasoning components—FPBG structures, base FSM/Regex compilers, MeTTa converters—are released under Apache License 2.0. Free to use, modify, and distribute with attribution.

## Commercial License
Advanced modules for GeoAI, time series forecasting, parallel Regex engines, and full AGI orchestration are proprietary. These require a commercial license. Redistribution or derivative use is restricted.

## Academic License
A Research Partner License (RPL) is also available for non-commercial academic research, requiring attribution and collaboration.

Contact: info@accelflare.com

Background & Experience

🧾 Background & Experience

The team brings over multiple decades of combined expertise in computer science, cognitive systems, embedded architectures, and high performance computing. Members have led advanced R&D in defense, aerospace, cyber security and AI acceleration, developing systems for real-time cognition, scene understanding, and embedded vision.

Highlights include:

• Development of FSM and Regex-based systems.

• Patents in AI hardware acceleration, data parsing, and neural network orchestration.

• International collaborations with Intel, Microchip, and others on FPGA/SoC-based AI systems.

• Open research contributions, including training-free generic AI algorithms.

• Experience designing cognitive decision architectures using graph-theoretic models.

• High Performance Computing for AI Mainframes, Cyber Security and Data Processing

Links and references

Direct References:

Accelflare.com

WO2022005409A1 - A method and apparatus for hardware accelerated data parsing, processing and enrichment - Patent

Team Portfolio Refs:

(PDF) Comparative Analysis and Modern Applications of PoW, PoS, PPoS Blockchain Consensus Mechanisms and New DLTs

WO2023121624A3 - Ensemble learning with parallel artificial neural networks in embedded and integrated systems - Patent

Dynamic Recursive Point Pattern Matching Algorithm for CBIR - File Exchange - MATLAB Central