Recursive Symbolic Intelligence with Auditable Adaptive Knowledge
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Contemporary artificial intelligence systems rely on opaque deep neural networks or static symbolic methods that lack continuous adaptability and verifiable record keeping, limiting their effectiveness in adaptive applications.
Innovation Solution
A recursive symbolic intelligence system, SESIS, autonomously generates and evolves its symbol set through real-time updates using multi-dimensional vectors, integrated with a tamper-evident ledger and meta-learning engine, employing cryptographic functions and multi-armed bandit algorithms for resource allocation, to continuously refine its internal representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If deep neural networks are used for AI processing, then adaptability and pattern recognition improve, but transparency and interpretability deteriorate due to opacity
Solution Approach 1:
The patent introduces symbolic representations as an intermediary layer between the neural network's numerical computations and the external world. These symbols serve as interpretable mediators that capture the essential meaning of neural network activations, enabling transparency without sacrificing the adaptability of deep learning. The symbolic layer translates continuous neural representations into discrete, human-understandable concepts.
Solution Approach 2:
The system segments the AI architecture into distinct functional layers: a neural network component for adaptive pattern recognition, a symbolic representation layer for interpretability, and a reasoning engine for logical processing. This segmentation allows each component to specialize in its strength while the integration resolves the contradiction between adaptability and transparency.
2Loss of information
If static symbolic methods are used for AI processing, then interpretability and reasoning improve, but continuous adaptability deteriorates
Solution Approach 1:
The patent makes the symbolic system dynamic by allowing symbolic representations to be created, modified, and refined continuously through interaction with the neural network and environmental feedback. The symbolic knowledge base is not fixed but evolves over time, enabling continuous adaptability while maintaining interpretability through the symbolic framework.
Solution Approach 2:
The system implements feedback loops where the symbolic reasoning engine evaluates neural network outputs, identifies inconsistencies or gaps, and guides further learning. This feedback mechanism enables continuous improvement and adaptation while the symbolic layer maintains interpretability by providing a structured framework for evaluating and refining knowledge.
3Adaptability or versatility
If hybrid approaches are used to combine neural networks and symbolic methods, then both adaptability and interpretability improve, but verifiable record keeping and continuous evolution deteriorate
Solution Approach 1:
The patent replaces traditional mechanical logging mechanisms with a blockchain-based distributed ledger. This substitution provides verifiable, tamper-proof record keeping of all symbolic operations and knowledge evolution events. The blockchain's cryptographic hashing and distributed consensus ensure reliability and auditability without adding significant complexity to the hybrid architecture.
4Stability of the object's composition
If a fixed ontology is imposed on the symbolic system, then structure and organization improve, but autonomy and self-evolution deteriorate
Solution Approach 1:
The patent implements a dynamic ontology where the structure of symbolic representations evolves automatically through learning from data and interaction. Rather than imposing a fixed taxonomy, the system allows categories, relationships, and symbolic meanings to emerge and refine themselves over time, maintaining structure through self-organization while enabling continuous evolution.
Solution Approach 2:
The symbolic system performs self-service by automatically organizing its own knowledge structure through unsupervised learning and reinforcement from environmental feedback. The system autonomously creates and refines its ontology without external intervention, balancing structure and evolution through self-directed organizational processes.
Data Source
AI summary
A recursive symbolic intelligence system is disclosed that employs continuously evolving symbolic nodes represented as multi-dimensional vectors with physical, cultural, and optionally functional sub-components. The system implements a mathematically defined recursive update function s(i)(t+1)=α·s(i)(t)+β·f(adj)({s(j)(t)})+γ·f(input)(v(i)), wherein α, β, and γ are tunable weighting factors; f(adj), aggregates contributions from semantically and topologically adjacent nodes; and f(input), processes incoming multi-modal input including text, audio, video, and sensor data. A tamper-evident ledger configured with a cryptographic hashing function such as SHA-256 records each symbolic update, and a scheduling module employing a multi-armed bandit algorithm together with a meta-learning engine utilizing covariance matrix adaptation evolution strategy dynamically optimizes processing resources and hyper-parameters. This system provides a continuous, adaptive, and auditable framework for dynamic knowledge representation applicable to domains such as autonomous systems, adaptive content generation, and symbolic legacy encoding.