Agent-oriented domain-specific language for the development of intelligentdistributed non-axiomatic reasoning agents

У дисертацији је представљен прототип агентског, домен-оријентисаног језика ALAS. Основни мотиви развоја ALAS језика су подршка дистрибуираном не-аксиоматском резоновању као и омогућавање интероперабилности и хетерогене мобилности Siebog агената јер је приликом анализе постојећих агентских домен-оријентисаних језика утврђено да ни један језик не подржава ове захтеве. Побољшање у односу на сличне постојеће агентске, домен-оријентисане језике огледа се и у програмским конструктима које нуди ALAS језик а чија је основна сврха писање концизних агената који се извршавају у специфичним доменима.U disertaciji je predstavljen prototip agentskog, domen-orijentisanog jezika ALAS. Osnovni motivi razvoja ALAS jezika su podrška distribuiranom ne-aksiomatskom rezonovanju kao i omogućavanje interoperabilnosti i heterogene mobilnosti Siebog agenata jer je prilikom analize postojećih agentskih domen-orijentisanih jezika utvrđeno da ni jedan jezik ne podržava ove zahteve. Poboljšanje u odnosu na slične postojeće agentske, domen-orijentisane jezike ogleda se i u programskim konstruktima koje nudi ALAS jezik a čija je osnovna svrha pisanje konciznih agenata koji se izvršavaju u specifičnim domenima.The dissertation presents the prototype of an agent-oriented, domainspecific language ALAS. The basic motives for the development of the ALAS language are support for distributed non-axiomatic reasoning, as well as enabling the interoperability and heterogeneous mobility of agents, because it is concluded by analysing existing agent-oriented, domainspecific languages, that there is no language that supports these requirements. The improvement compared to similar existing agentoriented, domain-specific languages are also reflected in program constructs offered by ALAS language, whose the main purpose is to enable writing the concise agents that are executed in specific domains

Multi-Agent Systems

This Special Issue ""Multi-Agent Systems"" gathers original research articles reporting results on the steadily growing area of agent-oriented computing and multi-agent systems technologies. After more than 20 years of academic research on multi-agent systems (MASs), in fact, agent-oriented models and technologies have been promoted as the most suitable candidates for the design and development of distributed and intelligent applications in complex and dynamic environments. With respect to both their quality and range, the papers in this Special Issue already represent a meaningful sample of the most recent advancements in the field of agent-oriented models and technologies. In particular, the 17 contributions cover agent-based modeling and simulation, situated multi-agent systems, socio-technical multi-agent systems, and semantic technologies applied to multi-agent systems. In fact, it is surprising to witness how such a limited portion of MAS research already highlights the most relevant usage of agent-based models and technologies, as well as their most appreciated characteristics. We are thus confident that the readers of Applied Sciences will be able to appreciate the growing role that MASs will play in the design and development of the next generation of complex intelligent systems. This Special Issue has been converted into a yearly series, for which a new call for papers is already available at the Applied Sciences journal’s website: https://www.mdpi.com/journal/applsci/special_issues/Multi-Agent_Systems_2019

A Generative Approach for Multi-Agent System Development

Abstract. The development of Multi-Agent Systems (MASs) involves special concerns, such as interaction, adaptation, autonomy, among others. Many of these concerns are overlapping, crosscut each other and the agent’s basic functionality. Over the last few years, several methodologies and implementation frameworks have been proposed to support agent-oriented software engineering. Although these approaches have brought some benefits to improve the productivity and quality on the MAS development, they present some restrictions. First, agent-oriented methodologies are too high level and do not indicate how to master the complexity of MAS concerns based on the object-oriented abstractions. Second, implementation frameworks provide object-oriented APIs for MAS development without providing guidelines for the modularization of agent concerns. Moreover, neither of the proposed agent oriented-approaches deals with the modeling and implementation of agent crosscutting concerns. This paper presents a generative approach for the development of MASs that addresses these restrictions. The proposed approach explores the MAS domain to enable the code generation of heterogeneous agent architectures. Aspect-oriented techniques are used to allow the modeling of crosscutting agent features. The generative approach brings several benefits to the code generation and modeling of agent crosscutting features since early development stages.