Multi-Station Decision Networks for Domain-Aware Predictive Decisions
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Solution Overview
Problem
Existing automated decision-making systems are typically industry-specific or domain-specific, lacking a unified approach to dynamically and predictively determine decisions across an entire ecosystem of software, users, and networks, and fail to leverage domain-specific knowledge effectively.
Innovation Solution
A decision network (DN) that connects disparate micro-decisions into a larger contextual model, utilizing Bayes' Law and predictive heuristics, allows for dynamic and predictive decision-making by integrating data from various sources and enabling granular control and governance, with visual and logical structures to manage complexity and ensure proper permissions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If industry-specific automated decision-making systems are used, then domain-specific knowledge can be effectively leveraged, but the system lacks a unified approach and cannot dynamically determine decisions across an entire ecosystem
Solution Approach 1:
The system is divided into multiple independent stations (data collection station, analytics station, decision station, execution station) that can be configured separately for different industries while forming a unified architecture. Each station handles specific functions and can be customized for domain-specific needs without affecting the overall system structure.
Solution Approach 2:
The decision network provides a universal platform that can be applied across multiple industries (oil and gas, manufacturing, healthcare, finance) by using a common architectural framework. The same core components serve different domains through configurable parameters and data sources, eliminating the need for completely separate systems for each industry.
2Extent of automation
If existing machine learning models are used, then flexibility to train for various industries is achieved, but the system cannot implement deterministic decision-making processes that leverage domain-specific knowledge
Solution Approach 1:
The system dynamically switches between deterministic rules and machine learning models based on the decision context. For critical decisions requiring determinism, the system uses configured rules. For predictive analytics, it employs ML models. This dynamic approach allows the same platform to provide both deterministic control and adaptive learning across different domains.
Solution Approach 2:
The analytics station acts as an intermediary between data collection and decision-making, processing information through both deterministic logic and machine learning models. This intermediary layer integrates domain-specific knowledge with automated decision-making, allowing experts to configure domain rules while the system automatically executes them through the standardized station architecture.
3Reliability
If a unified decision-making system is implemented, then high accuracy and timely responses can be achieved, but the system complexity increases
Solution Approach 1:
By segmenting the decision-making process into specialized stations (data collection, analytics, decision, execution), the system manages complexity through functional decomposition. Each station is simpler and more focused, yet together they form a comprehensive unified system that improves reliability through specialized processing at each stage.
Solution Approach 2:
The system uses nested station groups where complex decision networks can be organized hierarchically. Station groups can be nested within larger networks, allowing complex decisions to be broken down into manageable sub-decisions. This nesting structure reduces overall system complexity by organizing complexity in a hierarchical manner rather than a flat structure.
Data Source
AI summary
Systems and methods are provided for creating and executing a decision network that includes a number of stations configured to operate with respect to an instance of a document, container or other object. A user interface may be presented to a user that includes selectable options for configuring and connecting stations within the decision network, and for establishing executable logic for each station to modify or create data within fields of a container instance. Different stations in the decision network may be assigned different permissions with respect to the container. Individual stations in the decision network may be configured to determine a prediction or probability based on data accessible to the given station, where the prediction or probability of one station may be based in part on an earlier prediction or probability determined at another station of the decision network.