Advantages of Brahms for Specifying and Implementing a Multiagent Human-Robotic Exploration System

Abstract

We have developed a model-based, distributed architecture that integrates diverse components in a system designed for lunar and planetary surface operations: an astronaut’s space suit, cameras, all-terrain vehicles, robotic assistant, crew in a local habitat, and mission support team. Software processes (“agents”) implemented in the Brahms language, run on multiple, mobile platforms. These “mobile agents ” interpret and transform available data to help people and robotic systems coordinate their actions to make operations more safe and efficient. The Brahms-based mobile agent architecture (MAA) uses a novel combination of agent types so the software agents may understand and facilitate communications between people and between system components. A state-of-the-art spoken dialogue interface is integrated with Brahms models, supporting a speech-driven field observation record and rover command system. An important aspect of the methodology involves first simulating the entire system in Brahms, then configuring the agents into a runtime system Thus, Brahms provides a language, engine, and system builder’s toolkit for specifying and implementing multiagent systems. Background Multiagent systems were a natural outgrowth of knowledge-based systems of the 1970s, the idea of multiple, distributed sources of information and modelbased processing (“distributed AI”) developed in the 1980s, and the affordable, networked computing platforms of the 1990s. However, just as it has become practical to construct interacting systems of hardware and software, such as robotic assistants, GPS devices, biosensors, cameras, and the like, system builders need tools to help specify how these components are to interact in complex situations, means to test the designed processes, and an implementation architecture that is robust, modular, and amenable to runtime modifications (e.g., allowing components to leave or enter the system). We need a principled methodology for building multiagent systems (Alonso 2002). This paper describes how the Brahms simulation system