In this article, we propose a novel approach to building an aggregate assistant architecture that can handle complex user utterances through an evaluate-and-execute pipeline. Our proposed framework leverages a decentralized parser and machine learning algorithms to solve the distributed knowledge problem and enable efficient processing of user requests.
Evaluate-and-Execute Pipeline
The proposed architecture consists of an evaluate-and-execute pipeline, where the user’s request is first parsed into individual parts, and then each part is evaluated using a machine learning model to determine its relevance to the user’s intent. The results of these evaluations are then combined to generate a confidence score, which determines the likelihood that the user’s request will be successfully fulfilled.
Decentralized Parser
At the heart of our aggregate agent is a decentralized parser called TESS (Tensor-based Encoded Sample Selection), which enables efficient and accurate processing of user requests. TESS uses natural language processing techniques to analyze the user’s input and identify relevant sub-strings, or "samples," that can be used to build the user’s intent.
Machine Learning Algorithms
To solve the distributed knowledge problem, we employ machine learning algorithms that can handle complex user utterances and accurately identify their intended meaning. These algorithms include support vector machines (SVMs), which use kernel functions to map user inputs into a higher-dimensional space for classification, and neural networks, which can learn complex patterns in user behavior to improve intent detection.
Scoring and Selector Functions
Once the user’s request has been parsed and evaluated, we use scoring and selector functions to rank the most relevant intents based on their confidence scores. These functions are critical in determining the likelihood that a particular intent is correct and can be used to filter out irrelevant or ambiguous requests.
Sequencer Function
To generate an appropriate response to the user’s request, we use a sequencer function that takes into account the ranked list of intents and generates a sequence of possible responses. This function is essential in ensuring that the assistant provides relevant and coherent responses that meet the user’s intent.
Conclusion
In summary, our proposed aggregate assistant architecture offers a novel approach to handling complex user utterances through an evaluate-and-execute pipeline, decentralized parser, machine learning algorithms, scoring and selector functions, and sequencer function. By leveraging these components, our system can provide efficient and accurate processing of user requests, demystifying the complexities of natural language understanding for end-to-end intelligent automation.
