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A recent survey provides convincing evidence that JADE is among the most widely used tools to develop agent-based software systems. It finds application in industrial settings and to support research, and it has been used to introduce students to software agents in various universities. This paper offers a perspective on the current state of JADE by first presenting a chronicle of the relevant events that contributed to make JADE what it is today. Then, this paper enumerates some of the abstractions that JADE helped to identify and that are now commonly adopted in the community of researchers and practitioners interested in software agents and agent-based software development. Such abstractions have been successfully applied to construct relevant software systems, and among them, this paper reports on a mission-critical system that uses the abstractions that JADE contributed to identify to serve millions of users every day. Finally, this paper discusses an outlook on the near future of JADE by sketching a recent project that could contribute to provide a new perspective on the use of JADE.

Agent-based systems are increasingly used to support business-to-business (B2B) electronic commerce and other Internet-based transactions. The design complexity resulting from the multiple interconnected systems in these domains has to be managed in order to reduce costs and time to market. This paper introduces the Role-Algebraic Multi-Agent System Design (RAMASD) approach. RAMASD utilizes role models as reusable system-building blocks and a role algebra to capture the basic relations ofroles. A two-sorted algebra is used to define the role algebra's semantics. RAMASD reduces the complexity of designing agent-based B2B e-commerce systems by enabling designers to work at a high level of abstraction and by automatically allocating roles to agents according to applicable role models and design constraints. A case study concerning a B2B electronic market for the automotive industry demonstrates the applicability of RAMASD. The advantages and disadvantages of the proposed approach are discussed, and comparisons with relevant work are made.

Our goal in this paper is to introduce and motivate a methodology, called Tropos,1 for building agent oriented software systems. Tropos is based on two key ideas. First, the notion of agent and all related mentalistic notions (for instance goals and plans) are used in all phases of software development, from early analysis down to the actual implementation. Second, Tropos covers also the very early phases of requirements analysis, thus allowing for a deeper understanding of the environment where the software must operate, and of the kind of interactions that should occur between software and human agents. The methodology is illustrated with the help of a case study. The Tropos language for conceptual modeling is formalized in a metamodel described with a set of UML class diagrams.

The increasing demand for complex and distributed software calls for novel software engineering methods and techniques, to create systems able to autonomously adapt to dynamically changing situations. In this paper, we present a framework for engineering requirements for adaptive software systems. The approach, called Tropos4AS, combines goal-oriented concepts and high-variability design methods. The Tropos4AS requirements model can be directly mapped to software prototypes with an agent-oriented architecture which can be executed for requirements validation and refinement. We give a comprehensive description of the framework, with conceptual models, modelling guidelines, and supporting tools. The applicability of the framework to requirements validation and refinement is illustrated through a case study. Two controlled experiments with subjects provide an empirical evaluation of the proposed modelling language, with statistical evidence of the effectiveness of the modelling approach for gathering requirements of adaptive systems.

In this article we focus on the notion of artifact for agents in multi-agent systems (MAS) as a basis for a new meta-model promoting the modelling and engineering of agent societies and MAS environment as first-class entities. Its conceptual foundations lay upon theories and results coming from computational sciences as well as from organisational and cognitive sciences, psychology, computer supported cooperative work (CSCW), anthropology and ethology. In the resulting agents & artifacts (A&A) meta-model, agents are the (pro-)active entities in charge of the goals/tasks that altogether build up the whole MAS behaviour, whereas artifacts are the reactive entities providing the services and functions that make individual agents work together in a MAS, and that shape agent environment according to the MAS needs. After presenting the scientific background, we define the notions of artifact in the A&A meta-model, discuss how it affects the notion of intelligence in MAS, and show its application to a number of agent-related research fields.

The agent paradigm and multi-agent systems are a perfect match for the design of smart cities because of some of their essential features such as decentralization, openness, and heterogeneity. However, these major advantages also come at a great cost. Since agents’ mental states are hidden when the implementation is not known and available, intelligent services of smart cities cannot leverage information from them. We contribute with a proposal for the analysis and prediction of hidden agents’ mental states in a multi-agent system using machine learning methods that learn from past agents’ interactions. The approach employs agent communication languages, which is a core property of these multi-agent systems, to infer theories and models about agents’ mental states that are not accessible in an open system. These mental state models can be used on their own or combined to build protocol models, allowing agents (and their developers) to predict future agents’ behavior for various tasks such as testing and debugging them or making communications more efficient, which is essential in an ambient intelligence environment. This paper’s main contribution is to explore the problem of building these agents’ mental state models not from one, but from several interaction protocols, even when the protocols could have different purposes and provide distinct ambient intelligence services.

Agent interaction descriptions (or protocols) are a key aspect of the design of multi-agent systems. However, in the authors’ extensive experience, the notations commonly used for specification are both difficult to use, and lack expressiveness in certain areas. Some desired modular representations are impossible to express, while others result in specifications that are unwieldy and difficult to follow. In this paper we present a new notation for expressing interaction protocols, focussing on key issues that we have found to be problematic: the ability to define flexible data-driven protocols; representation of roles including their mapping to agents; and hierarchical modularity. We provide the semantics for our notation and illustrate its use with three diverse case studies. Finally we evaluate this notation using objectively assessable criteria that we argue contribute substantially to pragmatic usability, and using a human subject evaluation of the notation’s usability.

Smart communication protocols are becoming a key mechanism for improving communication performance in networks such as wireless sensor networks. However, the literature lacks mechanisms for simulating smart communication protocols in precision agriculture for decreasing production costs. In this context, the current work presents an agent-based simulator of smart communication protocols for efficiently managing pesticides. The simulator considers the needs of electric power, crop health, percentage of alive bugs and pesticide consumption. The current approach is illustrated with three different communication protocols respectively called (a) broadcast, (b) neighbor and (c) low-cost neighbor. The low-cost neighbor protocol obtained a statistically-significant reduction in the need of electric power over the neighbor protocol, with a very large difference according to the common interpretations about the Cohen’s d effect size. The presented simulator is called ABS-SmartComAgri and is freely distributed as open-source from a public research data repository. It ensures the reproducibility of experiments and allows other researchers to extend the current approach.

Agent-based computing is a promising approach for developing applications in complex domains. However, despite the great deal of research in the area, a number of challenges still need to be faced (i) to make agent-based computing a widely accepted paradigm in software engineering practice, and (ii) to turn agent-oriented software abstractions into practical tools for facing the complexity of modern application areas. In this paper, after a short introduction to the key concepts of agent-based computing (as they pertain to software engineering), we characterise the emerging key issues in multiagent systems (MASs) engineering. In particular, we show that such issues can be analysed in terms of three different “scales of observation”, i.e., in analogy with the scales of observation of physical phenomena, in terms of micro, macro, and meso scales. Based on this characterisation, we discuss, for each scale of observation, what are the peculiar engineering issues arising, the key research challenges to be solved, and the most promising research directions to be explored in the future.

Underwater sensors provide one of the possibilities to explore oceans, seas, rivers, fish farms and dams, which all together cover most of our planet’s area. Simulators can be helpful to test and discover some possible strategies before implementing these in real underwater sensors. This speeds up the development of research theories so that these can be implemented later. In this context, the current work presents an agent-based simulator for defining and testing strategies for measuring the amount of fish by means of underwater sensors. The current approach is illustrated with the definition and assessment of two strategies for measuring fish. One of these two corresponds to a simple control mechanism, while the other is an experimental strategy and includes an implicit coordination mechanism. The experimental strategy showed a statistically significant improvement over the control one in the reduction of errors with a large Cohen’s d effect size of 2.55.