Swarm Operator Control Section for Cognitive Support
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems for cyber-physical command-guided swarm (CGS) systems lack effective operator guidance and cognitive support, leading to inefficiencies in mission execution and decision-making, particularly in coordinating multiple disciplines and ensuring swarm integrity and cyber security.
Innovation Solution
A complex adaptive CGS system utilizing artificial intelligence, integrated information fusion/control diffusion, and a networked swarm of agent-controlled systems, with a multi-agent population employing game theoretic approaches and machine learning to enhance operator decision-making and system performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a swarm of semi-autonomous systems operates without centralized control, then system autonomy and scalability are improved, but coordination efficiency and decision-making speed deteriorate
Solution Approach 1:
The system segments control functions into multiple independent agents distributed across the swarm, including information fusion agents, control diffusion agents, and operator infusion agents. Each agent operates autonomously within its functional domain while contributing to collective decision-making, resolving the contradiction by enabling both high autonomy through distribution and efficient coordination through specialized agent roles
Solution Approach 2:
The patent introduces an operator control section as an intermediary that receives high-level mission objectives from human operators and translates them into actionable guidance for the swarm agents. This intermediary layer enables rapid decision-making by filtering and prioritizing information, while maintaining swarm autonomy through goal-oriented task decomposition rather than centralized micromanagement
2Adaptability or versatility
If multiple disciplines and components are integrated in the swarm system, then system capability and functionality are improved, but system complexity and coordination difficulty increase
Solution Approach 1:
The patent implements universal agent architectures that can perform multiple functions across different disciplines. The information fusion agents, for example, can process data from various sensor types (electro-optics, radar, radio frequency) using the same underlying algorithms. This multi-functionality approach enables diverse capabilities while maintaining consistent coordination protocols, reducing the complexity burden of integrating multiple disciplines
Solution Approach 2:
The system dynamically adjusts operational parameters such as agent communication frequency, information fusion depth, and control diffusion rates based on mission requirements and environmental conditions. This parameter adaptability allows the swarm to optimize coordination efficiency for different operational scenarios, managing complexity through contextual parameter adjustment rather than structural redesign
3Measurement precision
If real-time operator input is required for mission objectives, then operator effectiveness and control precision are improved, but system response time and operational efficiency deteriorate
Solution Approach 1:
The operator control section is pre-configured with mission objective templates and guidance protocols that enable rapid input of high-level directives. Rather than requiring detailed real-time commands, the system uses pre-defined objective structures that agents can immediately interpret and execute, achieving both operator precision and fast response by separating objective specification from tactical execution
Solution Approach 2:
The system implements continuous feedback loops where agents report status, sensor data, and mission progress to the operator control section in real-time. This feedback mechanism enables operators to provide precise guidance based on actual system state rather than theoretical conditions, improving control precision without requiring constant micromanagement. The feedback also allows autonomous adjustment of non-critical parameters, maintaining response time efficiency
Data Source
AI summary
Systems and methods are provided relating to a complex adaptive command guided swarm system including an operator section comprising a first command and control section and a plurality of networked swarm of semi-autonomously agent controlled system of systems platforms (SAASoSPs). The first command and control section includes a user interface, computer system, network interface, and plurality of command and control systems executed or running on the computer system. The networked SAASoSPs each include a second command and control section, wherein the second command and control section utilizes artificial intelligence (AI) configured with a combination of both symbolic and probabilistic machine learning for various functions including pattern recognition and new pattern identification. The AI is also configured to combine advice-based learning with active learning, wherein the AI solicits advice from a domain expert user via the first command and control section as necessary during both training and operational stages of the system.