Going beyond BDI for agent-based simulation

Larsen, John Bruntse

doi:10.1080/24751839.2019.1620024

Cited by 6 publications

(4 citation statements)

References 29 publications

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“…The researchers challenge the conclusions of prior surveys that suggest a lack of refined methodologies and tools as the primary factor, albeit a contributing factor, for the limited APLs adoption. Larsen [49] posits that there inadequacy in incentives for designers to transition to AOP, given that the functionalities exhibited by extant applications in the literature could be readily realized in conventional programming languages composed of minimal additional effort. In [50] present a succinct analysis of the historical and contemporary landscape of agent programming, with a particular focus on approaches based on the BDI model.…”

Section: Agent-based Applicationsmentioning

confidence: 99%

An Analysis of Multi Agent Systems Agent Based Programming

Кhusein

2023

JCNS

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The effectiveness of agent-based modeling as a simulation modeling methodology has resulted in its application in diverse settings, including the resolution of pragmatic business challenges, in recent times. The domain of symbolic artificial intelligence, which investigates intelligent and self-governing entities, is preoccupied with the mechanisms by which these entities arrive at determinations regarding their conduct in reaction to, or in expectation of, stimuli from the external environment. The scope of the methods employed encompasses a diverse array of techniques, spanning from negotiations to agent simulations, as well as multi-agent argumentation and planning. The present article scrutinizes the utilization of agent-based computing in multi-agent systems and provides an all-encompassing analysis of the relevant literature. This study delves into the examination of both traditional and contemporary agent programming languages, including their respective extensions, comparative analyses, and instances of their application in published literature.

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Section: Agent-based Applicationsmentioning

confidence: 99%

An Analysis of Multi Agent Systems Agent Based Programming

Кhusein

2023

JCNS

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“…There are three kinds of relationships in this multi-layered model. The first is Feeding data between service and data layer, the second is compositing and the third is interaction [27].…”

Section: Ontology On Cloud Computingmentioning

confidence: 99%

Unified Ontology Implementation of Cloud Computing for Distributed Systems

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Ibrahim

Sadeeq

2020

CJAST

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The ability to provide massive data storage, applications, platforms plus many other services leads to make the number of clouds services providers been increased. Providing different types of services and resources by various providers implies to get a high level of complexity. This complexity leads to face many challenges related to security, reliability, discovery, service selection, and interoperability. In this review, we focus on the use of many technologies and methods for utilizing the semantic web and ontology in cloud computing and distributed system as a solution for these challenges. Cloud computing does not have an own search engine to satisfy the needs of the providers of the cloud service. Using ontology enhances the cloud computing self-motivated via an intelligent framework of SaaS and consolidating the security by providing resources access control. The use RDF and OWL semantic technologies in the modeling of a multi-agent system are very effective in increases coordination the interoperability. One of the most efficient proposed frameworks is building cloud computing marketplace that collects the consumer's requirements of cloud services provider and managing these needs and resources to provide quick and reliable services.

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“…To enable the necessary runtime reasoning about others, we employ the BDI (beliefs, desires, intentions) model proposed for an individual cognitive agent [8,9] to provide a way to implement agents that use rich models of reasoning (cf. [10]) and that has been used in the context of complex decision making [11][12][13] and agent-based simulation [14][15][16]. The BDI model represents agents each possessing individual beliefs about the world and having desires that can be mapped to goal states the agent seeks to achieve.…”

Section: Motivationmentioning

confidence: 99%

Designing Multi-Agent System Organisations for Flexible Runtime Behaviour

Keogh

Sonenberg

2020

Applied Sciences

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We address the challenge of multi-agent system (MAS) design for organisations of agents acting in dynamic and uncertain environments where runtime flexibility is required to enable improvisation through sharing knowledge and adapting behaviour. We identify behavioural features that correspond to runtime improvisation by agents in a MAS organisation and from this analysis describe the OJAzzIC meta-model and an associated design method. We present results from simulation scenarios, varying both problem complexity and the level of organisational support provided in the design, to show that increasing design time guidance in the organisation specification can enable runtime flexibility afforded to agents and improve performance. Hence the results demonstrate the usefulness of the constructs captured in the OJAzzIC meta-model.

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Going beyond BDI for agent-based simulation

Cited by 6 publications

References 29 publications

An Analysis of Multi Agent Systems Agent Based Programming

An Analysis of Multi Agent Systems Agent Based Programming

Unified Ontology Implementation of Cloud Computing for Distributed Systems

Designing Multi-Agent System Organisations for Flexible Runtime Behaviour

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