Introducing Natural Language-based Instruction Protocol for Intelligent Machine Collaborations

The rapid development of various autonomous unmanned systems has increased the endogenous intelligence of machines, which empowers functional collaborations among intelligent machines. However, traditional protocols for intelligent machine collaboration have limitations regarding functionality, efficiency, and scalability. Moreover, existing research on intelligent machine collaboration is not yet approaching a unified paradigm that facilitates interactions among machines and between machines and humans. Therefore, aiming to enable a more efficient functional collaboration among intelligent machines, we propose a natural language-based instruction (NLI) protocol, which enjoys the advantages of autonomy, robustness and efficiency. In particular, we specify the NLI protocol architecture by introducing a corpus of NLIs, an NLI generation module, and an NLI parsing module, wherein the corpus contains control instructions, intents and slots, the generation module is used to generate NLIs, and the parsing module is used to parse the intents and slots in the instruction. Furthermore, we case-study the proposed NLI protocol in an electromagnetic interference avoidance scenario based on semi-physical simulation with software-defined radio. Simulation results show that the NLI protocol is more robust and efficient than traditional control protocol in severe wireless channels, which validates the feasibility and effectiveness of implementing intelligent machine collaboration based on NLI.