AI Agent Communication Tiers for Mission-Relevant Scene Exchange
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Solution Overview
Problem
Current communication infrastructures between AI agents at perception, cognition, and decision tiers are unsuitable due to independent study of non-text signal and semantic communication, lacking synergistic efforts, and state-of-the-art signal quality measures fail to quantify pragmatic value for specific missions, leading to inefficient bandwidth usage and irrelevant message transmission.
Innovation Solution
A communication system with a perception engine for encoding data, a cognition engine for selecting scene descriptions, and a decision engine for generating mission-relevant event descriptors, utilizing rate-distortion-power-delay optimization, rate-accuracy optimization, and rate-value optimization algorithms to prioritize mission-critical information transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all scene descriptions are transmitted at cognition tier, then complete information is provided, but bandwidth is wasted due to transmission of irrelevant messages
Solution Approach 1:
The patent extracts and transmits only the most relevant scene descriptions at the cognition tier by filtering out irrelevant information. The system identifies and transmits only those scene descriptions that are most relevant to the recipient's mission, thereby reducing bandwidth consumption while maintaining information completeness for critical tasks.
Solution Approach 2:
The patent applies local quality by differentiating the transmission quality based on scene description relevance. High-quality detailed descriptions are transmitted for mission-critical events, while lower-quality or summarized descriptions are used for less critical events, optimizing the balance between information completeness and bandwidth efficiency.
2Reliability
If non-text signal communication is used at perception tier, then direct perception is enabled, but bandwidth and processing resources are consumed
Solution Approach 1:
The patent implements dynamics by allowing flexible switching between perception tier (raw signals) and cognition tier (processed descriptions) based on real-time conditions. The system dynamically adjusts the communication tier depending on mission requirements, network conditions, and processing capabilities, optimizing the balance between perception accuracy and resource consumption.
3Measurement precision
If existing signal quality measures are used, then technical quality can be measured, but pragmatic value for specific missions cannot be quantified
Solution Approach 1:
The patent changes the measurement parameters from traditional signal quality metrics to mission-pragmatic value metrics. The system evaluates communication effectiveness based on whether transmitted information achieves mission objectives rather than solely based on signal fidelity, enabling quantification of pragmatic value for specific missions.
4Productivity
If independent study of non-text signal and semantic communication is pursued, then specialized optimization is achieved, but synergistic benefits are lost
Solution Approach 1:
The patent merges non-text signal communication and semantic communication into a unified framework that operates across perception, cognition, and decision tiers. The system combines the advantages of both approaches, enabling synergistic benefits by integrating raw signal processing with semantic understanding and mission-relevant information extraction.
Data Source
AI summary
The present invention provides a system for facilitating communication between AI agents by processing and interpreting real-time scene data in response to mission-related requests. The system includes sensory devices within an AI agent to collect environmental data and a large language model module to construct a mission-specific event dictionary. A perception engine processes the collected data, while a cognition engine extracts semantic information. A decision engine identifies mission-relevant events using the event dictionary. An integrated operating platform, comprising processors and memory, supports a multi-tiered communication framework: at the perception tier, encoded data is transmitted; at the cognition tier, selected scene descriptions are shared; and at the decision tier, mission-relevant event descriptors are communicated. This architecture ensures context-aware, tier-specific information exchange tailored to the recipient AI agent's mission needs.