Temporal Knowledge Graph Decision System
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Solution Overview
Problem
Existing decision support systems struggle to balance multiple conflicting objectives and account for time in dynamic environments, leading to suboptimal decision-making in complex domains like public safety and public services.
Innovation Solution
A data processing system utilizing temporal knowledge graphs to collect and organize information, forecast future states under different decisions, rate adherence to objectives, and consider trade-offs in a time-aware manner, incorporating machine learning and Pareto-front selection for efficient decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing decision support systems are used to handle multiple conflicting objectives, then decision-making can be performed, but the system cannot effectively balance multiple conflicting objectives and account for time in dynamic environments
Solution Approach 1:
The system segments the decision-making process into distinct modules: a temporal knowledge graph construction module that organizes historical and real-time data with temporal relationships, a forecasting module that predicts future states under different decisions, a rating module that evaluates adherence to multiple objectives, and a trade-off analysis module that considers time-aware compromises. This segmentation allows each module to specialize in handling specific aspects of multi-objective dynamic decision-making.
Solution Approach 2:
The system implements dynamics by using temporal knowledge graphs that capture evolving relationships over time, forecasting mechanisms that project future states, and time-aware trade-off considerations that adapt to changing conditions. The system continuously updates its understanding of the environment and re-evaluates decisions based on new information and evolving objectives.
2Productivity
If traditional decision support systems are used in complex domains like public safety, then basic decision-making is possible, but the systems lead to suboptimal outcomes due to inability to capture dynamic nature of situations
Solution Approach 1:
The system performs preliminary actions by constructing temporal knowledge graphs from historical and real-time data before actual decision-making occurs. It pre-calculates potential future states under different decision scenarios and pre-rates their adherence to multiple objectives. This preparation enables faster and more reliable decisions when actual choices need to be made in complex environments.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring real-time data, comparing actual outcomes with forecasted states, and using this information to refine temporal knowledge graphs and improve future forecasting accuracy. The system learns from past decisions and their outcomes, adapting its models to better capture the dynamic nature of complex environments like public safety scenarios.
Data Source
AI summary
A method for decision-making regarding a decision in an environment by a data processing system in view of multiple different objectives includes: collecting information within the environment, describing the information in at least one temporal knowledge graph (TKG), forecasting a future development of one future state or more future states at a future time or more future points in time, under different decisions by the at least one TKG. The method further describes each resulting future state/decision combination by a corresponding temporal knowledge graph, rates an adherence of each forecasted future state to each objective of the multiple different objectives, considers a trade-off between the objectives for decision-making in a time-aware manner; and provides the decision.