Designing a meta-model for a generic robotic agent system using Gaia methodology

Abstract

The emergence of multi-agent systems in the past years has led to the development of new methodologies to assist in the requirements and architectural analysis, as well as in the design phases of such systems. Consequently, several Agent Oriented Software Engineering (AOSE) methodologies have been proposed. In this paper, we analyze some AOSE methodologies, including Gaia, which supports the architectural design stage, and some proposed extensions. We then use an adapted version of this methodology to design an abstract generic system meta-model for a multi-robot application, which can be used as a basis for the design of these systems, avoiding or shortening repetitive tasks common to most systems. Based on the proposed Generic Robotic Agent Meta-Model (GRAMM), two distinct models for two different applications are derived, demonstrating the versatility and adaptability of the meta-model. By adapting the Gaia methodology to the design of open systems, this work makes the designers’ job faster and easier, decreasing the time needed to complete several tasks, while at the same time maintaining a high-level overview of the system.

Introduction

In the past years, systems that support the use of mobile robots have emerged at a growing rate. These systems are used in a wide range of areas, including military [19], medical [9], industrial [49], household applications [31], [30], and other smaller or more specific fields of research, such as the Robocup Rescue¹ and Soccer competitions,² as well as several projects currently under development. Despite the need to develop coordination methodologies among the several entities, the benefits of a system capable of working in a distributed manner (with simpler entities performing smaller tasks that together contribute to a larger task completion) are certainly greater than the complexities introduced by this model [1].

Several Agent-Oriented Software Engineering (AOSE) methodologies have been proposed over the years to help model multi-agent systems, some deriving from existing more traditional software engineering methodologies (usually object-oriented approaches), others with a more innovative origin. These methodologies are diverse in terms of the development phases they support, from requirement elicitation to implementation.

The main goal of the work presented in this paper is to introduce a meta-model for a generic robotic agent system, that can be used as a basis for the modeling of such systems, thus avoiding or shortening repetitive tasks common to most of these systems. This meta-model can be achieved by using a methodology that supports the architectural design stage. One such methodology is Gaia, a somewhat generic methodology that excludes requirements elicitation and implementation, focusing on analysis and design of the system [47]. Even though the Gaia methodology was originally intended to be used in the design of organizational systems, with a more defined set of hierarchical organization, it can also be used in the design of open systems, with minor modifications, as introduced in Section 4.

In the following section, we present a brief overview on some existing AOSE methodologies, as well as a brief analysis on some proposed extensions to the Gaia methodology (Section 2.4). In more detail, we present and analyze:

• the differences between the original Gaia methodology and the official proposed improvements

• the proposed AUML extensions

• the proposed ROADMAP extension

• the extensions and replacements as proposed by [10]

We also introduce a model for an extended version of the Gaia methodology, using the Software Process Engineering Metamodel (SPEM) 2.0 notation – see Section 3. This is believed to be an original contribution that provides a higher formalization and facilitates a better understanding of the Gaia methodology.

In Section 4, we introduce the proposed meta-model for a multi-agent system including several robotic vehicles and some peripheral agents using Gaia-based methodology. We then instantiate the Generic Robotic Agent Meta-Model (GRAMM) into two distinct designs for two different systems (Section 5).

Finally, on Section 6, we present some conclusions about the work that has already been done, as well as the guiding lines for the next steps to be taken.