Neurosymbolic Agent Routing for Deterministic Domain Problem Solving
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Solution Overview
Problem
Large Language Models (LLMs) exhibit non-deterministic behavior leading to inconsistency and inaccuracy in complex problem-solving tasks, particularly in domains like semiconductor processing, where predictable execution and precision are crucial.
Innovation Solution
The DANA system integrates domain-specific knowledge in both natural-language and symbolic forms, using a neurosymbolic approach to determine whether to process inputs through a symbolic processor or a neural network, generating deterministic outputs and mitigating the probabilistic nature of LLMs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If large language models are used for processing domain specific tasks, then the system can handle unstructured inputs and natural language questions, but the results become non-deterministic and inconsistent
Solution Approach 1:
The system segments the processing pipeline into distinct components: a neural network component for understanding unstructured natural language inputs, a symbolic processing component for deterministic reasoning, and a hybrid component that coordinates between them. This segmentation allows each component to specialize in what it does best while maintaining overall system reliability.
Solution Approach 2:
The patent introduces symbolic processing as an intermediary layer between the probabilistic neural network and the final output. This intermediary translates the non-deterministic neural network outputs into deterministic symbolic representations, ensuring consistent and predictable results while preserving the ability to process unstructured inputs.
2Reliability
If symbolic processing is used exclusively, then deterministic and predictable results are achieved, but the system cannot effectively process unstructured natural language inputs
Solution Approach 1:
The system merges symbolic processing with neural network processing in a hybrid architecture. The neural network handles unstructured natural language understanding while the symbolic processor ensures deterministic reasoning and output. This combination allows the system to process unstructured inputs effectively while maintaining deterministic execution through the integrated symbolic component.
3Ease of manufacture
If domain specific knowledge is stored in natural language form, then the system maintains flexibility and ease of updating, but processing accuracy and precision decrease
Solution Approach 1:
The system transforms domain knowledge from natural language form into structured symbolic representations with defined parameters and relationships. This parameterization maintains the flexibility of natural language knowledge bases while enabling precise symbolic processing. The structured format allows for accurate reasoning while keeping knowledge updates relatively straightforward through parameter modifications.
Data Source
AI summary
A system receives domain specific questions from users and answers them. The system stores domain specific information comprising domain specific facts and domain specific programs. The system receives an input request to perform a domain specific task for the particular domain. The system provides the input request to a machine learning model trained to predict a score indicating whether the input request should be processed by a symbolic processor or by a neural network. If the score predicted by the machine learning model indicates that the input request should be processed by the symbolic processor, the system determines whether a stored domain specific program can solve the input request. If none of the stored domain specific programs can solve the input request, the system generates a new program for solving the input request using a machine learning based language model and the set of domain specific facts.