Integrating Semantic Communication and Human Decision-Making into an End-to-End Sensing-Decision Framework

2024-12-06

arXiv.org

AI Summary

This study addresses the fundamental disconnect between semantic communication and human decision-making (HDM), which impedes end-to-end perception-decision modeling and causes semantic information supply-demand mismatch. We propose the first unified probabilistic framework integrating semantic communication with HDM. Methodologically: (i) we jointly model perception, semantic encoding, channel transmission, and cognitive decision-making using probabilistic graphical models; (ii) we design a human-factor-driven semantic feature abstraction mechanism that balances task requirements with cognitive load; and (iii) we formulate a bandwidth-cognition co-optimization objective to enable demand-aware semantic filtering and efficient transmission. Experiments demonstrate that our framework significantly reduces bandwidth (−62%), energy consumption (−58%), and latency (−49%), while improving human decision accuracy (+7.3%) and response speed (−19%). This work establishes an interpretable, optimization-enabled theoretical foundation and technical pathway for intelligent human-machine collaborative systems.

Technology Category

• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Machine Learning: Probabilistic Circuits and Graphical Models

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

As early as 1949, Weaver defined communication in a very broad sense to include all procedures by which one mind or technical system can influence another, thus establishing the idea of semantic communication. With the recent success of machine learning in expert assistance systems where sensed information is wirelessly provided to a human to assist task execution, the need to design effective and efficient communications has become increasingly apparent. In particular, semantic communication aims to convey the meaning behind the sensed information relevant for Human Decision-Making (HDM). Regarding the interplay between semantic communication and HDM, many questions remain, such as how to model the entire end-to-end sensing-decision-making process, how to design semantic communication for the HDM and which information should be provided to the HDM. To address these questions, we propose to integrate semantic communication and HDM into one probabilistic end-to-end sensing-decision framework that bridges communications and psychology. In our interdisciplinary framework, we model the human through a HDM process, allowing us to explore how feature extraction from semantic communication can best support human decision-making. In this sense, our study provides new insights for the design/interaction of semantic communication with models of HDM. Our initial analysis shows how semantic communication can balance the level of detail with human cognitive capabilities while demanding less bandwidth, power, and latency.

Problem

• Model end-to-end sensing-decision process for semantic communication.
• Design semantic communication to support human decision-making effectively.
• Balance semantic information detail with human cognitive capabilities.

Innovation

• Integrates semantic communication with human decision-making
• Develops probabilistic end-to-end sensing-decision framework
• Balances semantic detail with human cognitive capabilities