面向异构语义原生通信的贝叶斯逆上下文推理
Bayesian Inverse Contextual Reasoning for Heterogeneous Semantics-Native Communication
AI总结：
针对异构语义原生通信中智能体上下文不同导致上下文推理有效性下降的问题，提出贝叶斯逆上下文推理和贝叶斯逆线性化上下文推理两种方法，后者在计算效率、推理精度和通信效果上更优，为相关场景提供了有效解决方案。
摘要：
本研究针对异构语义原生通信（SNC）问题展开。当智能体不共享相同通信上下文时，上下文推理（CR）的有效性会受损，因此需要智能体推断其他智能体的上下文。本文提出了一种新颖框架，采用两种贝叶斯推理方法求解SNC中的CR逆问题，分别为贝叶斯逆CR（iCR）和贝叶斯逆线性化CR（iLCR）。第一种贝叶斯iCR方法利用马尔可夫链蒙特卡洛（MCMC）采样来推断智能体的上下文，但计算成本较高。为解决这一问题，研究采用贝叶斯iLCR方法，该方法通过训练线性神经网络得到线性化CR（LCR）模型。实验结果表明，与贝叶斯iCR相比，贝叶斯iLCR方法所需计算量更少，且推理精度更高。此外，基于贝叶斯iLCR方法所得上下文的异构SNC，其通信有效性优于贝叶斯iCR方法。总体而言，本研究为提升智能体上下文不同场景下的SNC有效性提供了有价值的思路与方法。
英文摘要：
This work deals with the heterogeneous semantic-native communication (SNC) problem. When agents do not share the same communication context, the effectiveness of contextual reasoning (CR) is compromised calling for agents to infer other agents' context. This article proposes a novel framework for solving the inverse problem of CR in SNC using two Bayesian inference methods, namely: Bayesian inverse CR (iCR) and Bayesian inverse linearized CR (iLCR). The first proposed Bayesian iCR method utilizes Markov Chain Monte Carlo (MCMC) sampling to infer the agent's context while being computationally expensive. To address this issue, a Bayesian iLCR method is leveraged which obtains a linearized CR (LCR) model by training a linear neural network. Experimental results show that the Bayesian iLCR method requires less computation and achieves higher inference accuracy compared to Bayesian iCR. Additionally, heterogeneous SNC based on the context obtained through the Bayesian iLCR method shows better communication effectiveness than that of Bayesian iCR. Overall, this work provides valuable insights and methods to improve the effectiveness of SNC in situations where agents have different contexts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
