Bayesian Agent Template Framework for IoT
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Solution Overview
Problem
Existing methods for developing Bayesian agents are not easily reusable across applications and require complex coding, making them inefficient for quick development and scalable implementation, especially in environments with uncertain or noisy data.
Innovation Solution
A framework for modeling and instantiating Bayesian agents using templates with node-level and template-level metadata, allowing for decoupling of software-defined sensors and actuators, and enabling scalable and reusable Bayesian network interactions with the environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If Bayesian agents are developed ad hoc, then they can be customized for specific applications, but reusability across applications is poor
Solution Approach 1:
The patent segments the Bayesian agent into reusable components: a template system for the Bayesian network structure and separate runtime data for specific instantiations. This allows the same template to be reused across applications with different data, improving reusability while reducing development complexity through standardized components.
Solution Approach 2:
The template system provides a universal framework that can instantiate different Bayesian agents across multiple applications. The template contains the reusable Bayesian network structure and metadata definitions, while runtime data provides application-specific customization, enabling one template to serve multiple functions and applications.
2Productivity
If manual coding is used for Bayesian agents, then customization is possible, but development time is excessive
Solution Approach 1:
The template is prepared in advance with the Bayesian network structure, node definitions, and metadata templates. This preliminary action eliminates the need for manual coding during development, as users only need to provide runtime data instead of writing code, significantly increasing productivity while reducing coding requirements.
Solution Approach 2:
The system uses template copying where a predefined template structure is copied and instantiated with runtime data. This avoids manual creation of Bayesian networks from scratch, allowing users to leverage pre-built templates and significantly reduce development time and coding effort.
3Reliability
If sensors and actuators are coupled with Bayesian network nodes, then integration is tight, but updates to sensors/actuators require template changes
Solution Approach 1:
The patent segments the system into three independent parts: the template (containing Bayesian network structure), the sensors/actuators (software-defined components), and runtime data (instance-specific information). This segmentation allows sensors and actuators to be updated independently of the template, improving update flexibility while maintaining reliable integration through structured metadata definitions.
Solution Approach 2:
The template acts as an intermediary layer between the Bayesian network structure and the sensors/actuators. It defines the interface and metadata for sensor-actuator connections without requiring direct coupling, allowing updates to sensors/actuators without changing the template while maintaining reliable integration through the structured metadata layer.
Data Source
AI summary
According to a first aspect, the invention relates to a tool for modelling, instantiating and/or executing a Bayesian agent in an application. The tool comprises a modelling module which is adapted to enable a user to determine a template for the Bayesian agent. The template comprises a Bayesian network, which comprises nodes and/or node-level meta-data. The node-level meta-data at least defines node behavior and/or an association of a node with a software defined sensor and/or one or more software defined actuators. Also the template comprises template-level metadata at least defining lifecycle properties for the Bayesian agent. The template is suitable to be instantiated and executed as the Bayesian agent instance.