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.

本研究提出了一种解决 SNC 中 CR 问题的框架，使用两种贝叶斯推理方法：贝叶斯反向 CR 和贝叶斯反向线性化 CR 方法，实验结果表明，贝叶斯反向线性化 CR 方法相对于贝叶斯反向 CR 方法而言，需要更少的计算量并且具有更高的推理准确性，并且基于该方法获得的异构 SNC 显示出比基于贝叶斯反向 CR 方法所获得的上下文更好的通信效果。