Multiple-Agent Engagement Decision Matrix for Real-Time Coordination
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Solution Overview
Problem
Existing Multiple Agent Engagement Decision Systems (MAEDS) face challenges in making timely, coordinated, and resource-efficient decisions for multiple client engagements, particularly in dynamically changing environments, leading to hasty decisions that waste agent capability and reduce engagement options.
Innovation Solution
A real-time MAEDS that determines agent/client pairs in priority tiers, evaluates engagement options using a value matrix, and adjusts for urgency and risk to make coordinated decisions, ensuring timely and flexible engagement strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple autonomous agents engage multiple clients simultaneously, then service coverage and engagement capacity are improved, but coordination complexity and decision-making difficulty increase
Solution Approach 1:
The system segments the multiple agent-client engagements into discrete engagement options, each representing a specific pairing. The MAEDS evaluates these segmented options individually using a scoring mechanism, then selects the optimal combination. This segmentation transforms the complex coordination problem into manageable discrete decisions.
Solution Approach 2:
The patent introduces a new dimensional framework by creating a matrix that crosses agents with clients, where each cell represents an engagement option. This matrix dimensionality allows the system to simultaneously consider multiple agent-client pairings and their interactions, resolving coordination complexity through structured dimensional representation.
2Adaptability or versatility
If real-time engagement decisions are made dynamically, then adaptability to changing client environment is improved, but decision-making time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-defining engagement options and pre-establishing the matrix structure before actual engagement decisions are needed. When real-time decisions are required, the system already has the framework in place, allowing rapid evaluation and selection without computationally expensive on-the-fly calculations.
Solution Approach 2:
The MAEDS is designed to autonomously evaluate engagement options and make decisions without external managerial intervention. The system self-manages the complex real-time decision-making process by automatically scoring options, selecting optimal engagements, and updating the matrix, thereby reducing both decision-making time and external coordination overhead.
3Productivity
If engagement decisions are made without managerial intervention, then operational efficiency and response speed are improved, but risk of hasty decisions and resource waste increases
Solution Approach 1:
The system incorporates feedback mechanisms where the MAEDS continuously monitors engagement outcomes and uses this information to refine future engagement decisions. The matrix is dynamically updated based on observed results, allowing the autonomous system to learn from past decisions and improve reliability over time without requiring managerial oversight.
Solution Approach 2:
The patent changes key parameters by introducing a scoring mechanism that quantifies engagement quality across multiple dimensions. This parameter transformation converts subjective decision-making into objective evaluation, allowing the autonomous MAEDS to make reliable decisions by optimizing numerical scores rather than relying on human judgment, thereby maintaining high decision quality while operating without managerial intervention.
Data Source
AI summary
Agent/client pairs in at least one priority tier are determined. A matrix value is determined for each agent/client pair based on the client rank of the agent/client pair. An engagement capability parameter is determined for each agent/client pair. The matrix value for each disallowed agent/ranked client value is set to a zero value. All possible engagement options are evaluated. Candidate pair paths are determined by determining pair paths having a highest initial path value. The initial path value of each candidate pair path is decreased based on agent/client pairs in the candidate pair path that are urgent agent/client pairs or risky agent/client pairs to derive a final path value for each candidate pair path. A best path is determined based on the final path value for each candidate pair path. At least one engagement decision is derived based on the best path and transmitted towards agents in the best path.