Thing Machine Knowledge Base Graph Structure
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Solution Overview
Problem
Current systems lack an efficient method for a Thing Machine to perform models involving administering units of memory with non-mutable components organized as a graph, where nodes represent definitional and procedural knowledge, and verbs act upon components within a domain of discourse.
Innovation Solution
The Thing Machine employs a system where units of memory are structured as a graph with nodes and edges, enabling verbs to perform actions on components, with a NeurBot acting as an independent AI agent that self-directs the assembly of a performable vocabulary to act upon and interact with other Things within the graph.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a Thing Machine performs models by administering units of memory with non-mutable components organized as a graph, then the system can represent complex systems and data structures effectively, but the system complexity and difficulty of operation increase
Solution Approach 1:
The knowledge base is segmented into discrete units of memory called Things, each with non-mutable components. These Things are organized as a graph structure with nodes and edges, allowing complex systems to be represented through composition of simpler elements. The segmentation principle resolves the contradiction by enabling complex representation through structured composition rather than monolithic complexity.
Solution Approach 2:
A vocabulary layer is introduced as an intermediary between the graph structure and performable actions. The vocabulary contains Things representing performable actions and their domains, serving as a mediator that translates high-level operations into graph manipulations. This intermediary layer simplifies operation while maintaining the ability to represent complex systems.
2Productivity
If verbs are used to perform actions on components within a domain of discourse, then the system can execute model operations, but the difficulty of detecting and measuring system state increases
Solution Approach 1:
The system incorporates evaluation mechanisms that provide feedback on the state of Things in the knowledge base. When verbs perform actions on components, the graph structure and vocabulary enable tracking of state changes through defined relationships and domains. This feedback mechanism resolves the contradiction by making system state detectable and measurable through structured observations.
3Extent of automation
If a NeurBot self-directs the assembly of a performable vocabulary, then the system achieves learning and self-direction capabilities, but the time and computational resources required increase
Solution Approach 1:
The system pre-structures the knowledge base as a graph with Things representing vocabulary items, performable actions, and domains. This preliminary organization enables the NeurBot to self-direct assembly by selecting and combining pre-defined components rather than creating everything from scratch. The preliminary action principle resolves the contradiction by reducing assembly time through pre-prepared building blocks.
Data Source
AI summary
A system and method for performing a desired verb action involves administering units of memory having a set of non-mutable components in a knowledge base. The components are organized as a graph with nodes and edges extending between the nodes. The nodes correspond to the components and are representative of definitional knowledge and procedural knowledge of a model. The model has a component representative of a vocabulary of components including components representative of performable actions and components a performable action can act upon. A verb is located in the vocabulary satisfying a criteria of the desired verb action. The verb action is performed in a context of a domain of discourse. The domain of discourse includes a set of components the performable action can act upon. The domain of discourse includes the vocabulary.