A Review of Agent-Based Programming for Multi-Agent Systems

Cardoso, Rafael C.; Ferrando, Angelo

doi:10.3390/computers10020016

Cited by 57 publications

(28 citation statements)

References 83 publications

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“…Representatives of these classes include cognitive-oriented languages such as AgentSpeak(L)/Jason [24], agent communication and environment modelling languages like KQML [25], CArtAgO [26], and SARL [27], and organisation-oriented programming such as MOISE [28]. Another recent survey by Cardoso et al [29] distinguishes between general-purpose agent programming languages and languages for agent-based modeling and simulation. In the following, we cover a programming model arising from research on spatial computing [6,30] and field-based coordination [7,31], which provides an original approach to MAS programming that allows driving micro-level activity based on specifications addressing the meso-and macro-levels of a MAS.…”

Section: Multi-agent Systems Programmingmentioning

confidence: 99%

A Programming Approach to Collective Autonomy

Casadei

Aguzzi

Viroli

2021

JSAN

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Research and technology developments on autonomous agents and autonomic computing promote a vision of artificial systems that are able to resiliently manage themselves and autonomously deal with issues at runtime in dynamic environments. Indeed, autonomy can be leveraged to unburden humans from mundane tasks (cf. driving and autonomous vehicles), from the risk of operating in unknown or perilous environments (cf. rescue scenarios), or to support timely decision-making in complex settings (cf. data-centre operations). Beyond the results that individual autonomous agents can carry out, a further opportunity lies in the collaboration of multiple agents or robots. Emerging macro-paradigms provide an approach to programming whole collectives towards global goals. Aggregate computing is one such paradigm, formally grounded in a calculus of computational fields enabling functional composition of collective behaviours that could be proved, under certain technical conditions, to be self-stabilising. In this work, we address the concept of collective autonomy, i.e., the form of autonomy that applies at the level of a group of individuals. As a contribution, we define an agent control architecture for aggregate multi-agent systems, discuss how the aggregate computing framework relates to both individual and collective autonomy, and show how it can be used to program collective autonomous behaviour. We exemplify the concepts through a simulated case study, and outline a research roadmap towards reliable aggregate autonomy.

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Section: Multi-agent Systems Programmingmentioning

confidence: 99%

A Programming Approach to Collective Autonomy

Casadei

Aguzzi

Viroli

2021

JSAN

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“…As noted in recent agent programming reviews and surveys [14][15][16], there are still many open challenges that prevent agent programming languages to be used in the robotic domain. Some of these challenges include:…”

Section: Perspective Of the Authorsmentioning

confidence: 99%

Agents and Robots for Reliable Engineered Autonomy:A Perspective from the Organisers of AREA 2020

Cardoso

Ferrando

Briola

et al. 2021

JSAN

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Multi-agent systems, robotics and software engineering are large and active research areas with many applications in academia and industry. The First Workshop on Agents and Robots for reliable Engineered Autonomy (AREA), organised the first time in 2020, aims at encouraging cross-disciplinary collaborations and exchange of ideas among researchers working in these research areas. This paper presents a perspective of the organisers that aims at highlighting the latest research trends, future directions, challenges, and open problems. It also includes feedback from the discussions held during the AREA workshop. The goal of this perspective is to provide a high-level view of current research trends for researchers that aim at working in the intersection of these research areas.

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“…The use of ontologies and knowledge graphs (KG) provides slower solutions, but allows use of existing tools, such as SPARQL [45]. An agent-Based approach also can be used [46]. Since the models are quite complex, it is advisable to implement them as cloud services.…”

Section: Architectural Solutions Of Dasmentioning

confidence: 99%

Using the Context-Sensitive Policy Mechanism for Building Data Acquisition Systems in Large Scale Distributed Cyber-Physical Systems Built on Fog Computing Platforms

et al. 2021

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The article deals with the use of context-sensitive policies in the building of data acquisition systems in large scale distributed cyber-physical systems built on fog computing platforms. It is pointed out that the distinctive features of modern cyber-physical systems are their high complexity and constantly changing structure and behavior, which complicates the data acquisition procedure. To solve this problem, it is proposed to use an approach according to which the data acquisition procedure is divided into two phases: model construction and data acquisition, which allows parallel realization of these procedures. A distinctive feature of the developed approach is that the models are built in runtime automatically. As a top-level model, a multi-level relative finite state operational automaton is used. The automaton state is described using a multi-level structural-behavioral model, which is a superposition of four graphs: the workflow graph, the data flow graph, the request flow graph and the resource graph. To implement the data acquisition procedure using the model, the context-sensitive policy mechanism is used. The article discusses possible approaches to implementation of suggested mechanisms and describes an example of application.

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A Review of Agent-Based Programming for Multi-Agent Systems

Cited by 57 publications

References 83 publications

A Programming Approach to Collective Autonomy

A Programming Approach to Collective Autonomy

Agents and Robots for Reliable Engineered Autonomy:A Perspective from the Organisers of AREA 2020

Using the Context-Sensitive Policy Mechanism for Building Data Acquisition Systems in Large Scale Distributed Cyber-Physical Systems Built on Fog Computing Platforms

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