Self-Programming Machine Attractor-Based Process Model
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Solution Overview
Problem
Current artificial general intelligence (AGI) systems lack true self-programming abilities and scalability to produce complex and useful AGI systems, despite research indicating that autopoietic machines could exhibit key AGI characteristics.
Innovation Solution
A method for constructing a self-programming machine that emulates a complex dynamical system by determining a target attractor, constructing a process model, and configuring logical processing units to form and manage bindings between event processing nodes, allowing the machine to generate, send, and receive event signals, process information, and adapt to external environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If autopoietic machines are constructed to achieve self-programming abilities, then the machine gains autonomous learning and adaptation capabilities, but the device complexity and difficulty of construction increase significantly
Solution Approach 1:
The patent divides the complex autopoietic machine into multiple simpler components: logical processing units, process models, event processing nodes, and interaction regions. Each component has a specific function, and they work together through standardized interfaces (bindings) to achieve the overall self-programming capability without requiring the entire system to be constructed as a single complex unit.
Solution Approach 2:
The patent implements a hierarchical structure where process models contain event processing nodes, which in turn contain event signals and data. Multiple instances of process models are nested within the logical processing units, creating a layered architecture that manages complexity by organizing components at different levels of abstraction.
2Productivity
If multiple instances of process models are created to emulate complex dynamical systems, then the system's computational capability increases, but the quantity of components and system complexity increase
Solution Approach 1:
The patent creates universal process models that can be instantiated multiple times to handle different computational tasks. Each process model instance is a copy of the same template structure, allowing the system to scale computational capability by replicating proven functional units rather than designing unique components for each task.
Solution Approach 2:
The patent employs replication of process model instances to increase computational capacity. Instead of building entirely new components, the system creates copies of validated process models and connects them through bindings, allowing rapid scaling of computational capability while reusing the same structural blueprints.
3Reliability
If bindings are formed between event processing nodes to enable information flow, then the system achieves coordinated processing, but the complexity of managing interactions between components increases
Solution Approach 1:
The patent introduces bindings as intermediary mechanisms that mediate interactions between event processing nodes and process models. These bindings act as standardized interfaces that simplify connection management by providing uniform protocols for information flow, reducing the complexity of directly managing point-to-point interactions between all components.
Solution Approach 2:
The patent implements feedback loops through the binding mechanism, where event signals flow through bindings and return information to previous nodes. This allows the system to maintain coordinated processing by continuously monitoring and adjusting information flow through the binding network, ensuring reliability without requiring complex centralized control.
Data Source
AI summary
A method for constructing a self-programming machine comprises determining a target attractor of a complex dynamical system, the attractor comprising event processing nodes and event flows propagating between the nodes, and constructing a fragmented process model of the attractor having event processing nodes corresponding to nodes of the attractor. Logical processing units are constructed, each comprising a communications interface and an input/output for receiving/sending information from/to an external environment or process. Instances of the process model are created and associated with the processing units with interaction regions interposed between the instances. The processing units are configured to (i) form bindings between nodes of the instances via the interaction regions in accordance with attractor event flows, (ii) generate, send and receive event signals via the communications interfaces in accordance with the bindings (iii) process event signals received via the communications interfaces and information from their inputs to determine output information.