Multi-Agent Dynamic Situational Awareness Model
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Solution Overview
Problem
Existing network-connected sensing platforms face challenges in effectively communicating and coordinating a shared situational model across dispersed, autonomous agents, which limits their ability to operate independently and maintain a coherent understanding of their environment.
Innovation Solution
A hierarchical framework that enables agents to communicate and process sensor data, transforming it into a shared situational model that can be updated dynamically, allowing for varying levels of abstraction and enabling agents to operate independently by falling back on local processing and caching data for communication when reconnected.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If agents operate independently in dispersed environments, then system reliability is improved, but coordination and shared situational awareness deteriorate
Solution Approach 1:
The system segments the global situational model into local models maintained by individual agents. Each agent maintains a local model of its environment and communicates updates to others, allowing independent operation while preserving shared awareness through distributed model maintenance.
Solution Approach 2:
Agents continuously exchange situational model updates with each other, creating feedback loops that synchronize local models with the global situation. This feedback mechanism ensures that even dispersed agents maintain accurate shared situational awareness without constant central coordination.
2Adaptability or versatility
If a hierarchical structure is implemented for varying levels of abstraction, then adaptability is improved, but device complexity increases
Solution Approach 1:
The hierarchical structure is dynamic rather than static. Agents can move between hierarchy levels based on task requirements, and the abstraction levels are adjusted on-demand. This dynamic hierarchy maintains adaptability while reducing structural complexity by only activating higher levels when needed.
Solution Approach 2:
The system implements partial hierarchies where not all agents operate at every level. Lower-level agents maintain detailed local models while higher-level agents maintain abstracted global models. This partial implementation of hierarchy provides necessary adaptability without the full complexity of a complete multi-level structure.
3Measurement precision
If the situational model is updated continuously, then measurement precision is improved, but loss of time increases
Solution Approach 1:
Instead of continuous updates, the system uses periodic updates triggered by significant events or time intervals. The situational model is updated at appropriate intervals to maintain precision while reducing the time overhead of constant updates. Agents synchronize their models at key moments rather than continuously.
Data Source
AI summary
A method, system, and non-transitory computer-readable medium, the method including receiving notifications from a plurality of agents, the notifications being associated with the plurality of agents sensing aspects of an environment; determining, based at least in part on the received notifications from the plurality of agents, a situational model of the environment from the notifications; determining a status of the environment based on the situational model; and reporting the status of the environment to at least one of the plurality of agents.