Rule-Based Management of Adaptive Agents for Dynamic Recommendations
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Solution Overview
Problem
Existing automated decisioning systems lack effective management and maintenance capabilities, as they are based on static predictive models that do not adapt or learn from experience, limiting their ability to respond dynamically to changing data and business rules.
Innovation Solution
A system utilizing adaptive agents that learn from prior outcomes, managed through a rules-based framework, allowing for dynamic configuration, maintenance, and feedback-driven updates, enabling real-time recommendations and improved decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static predictive models are used in automated decisioning systems, then system simplicity and ease of operation are maintained, but adaptability to changing business conditions and data is lost
Solution Approach 1:
The patent implements adaptive agents that dynamically adjust their predictions based on feedback from actual outcomes. The agents continuously learn from past performance and modify their internal models, transforming static predictive systems into dynamic ones that automatically adapt to changing business conditions without requiring manual reconfiguration.
Solution Approach 2:
The system incorporates feedback mechanisms where actual outcomes are fed back to adaptive agents to refine their predictions. This feedback loop enables the system to learn from past decisions and improve accuracy over time, resolving the contradiction between adaptability and complexity by automating the learning process.
2Productivity
If manual updates are required for predictive models, then system reliability is maintained through human oversight, but productivity and response time are reduced
Solution Approach 1:
The adaptive agents perform self-updates by automatically learning from past outcomes and refining their own predictions. This self-service capability eliminates the need for manual model updates, significantly improving productivity and response time while maintaining reliability through continuous automated learning and adaptation.
3Measurement precision
If multiple adaptive agents are deployed to improve decision quality, then measurement precision and prediction accuracy are enhanced, but device complexity and management difficulty increase
Solution Approach 1:
The system divides the prediction function into multiple specialized adaptive agents, each handling specific aspects of decision-making. This segmentation allows for improved measurement precision through specialized processing while managing complexity by organizing agents in a modular architecture that can be independently managed and configured.
Data Source
AI summary
A computer-implemented method described for making a recommendation with respect to a plurality of items using a plurality of adaptive models or agents. The described method includes receiving one or more business rules; receiving a request for a recommendation; receiving attributes relating to the request; activating one or more adaptive agents such that each activated adaptive agent generates one or more recommendations with respect to the items based at least in part on an evaluation of prior outcomes relating to the items; selecting from among the one or more recommendations at least one final recommendation; and displaying the at least one final recommendation to a user. Related apparatus, systems, techniques and articles are also described.