Dynamic Contextual Regulations in Open Multi-agent Systems

Felicíssimo, Carolina Howard

doi:10.1007/11926078_75

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…DynaCIP's main idea for continuously supporting agents with updated contextual information, based on both a do-main ontology instance and compositions of rules, seems to be suitable for MASs from different domains. For the domain of normative MASs, instead of contextual protocol information, contextual norms are provided as precise inputs for enforcement mechanisms [5]. For the domain of pervasive mobile applications, policies are applied according to agents' contexts, which are determined based on agents' physical locations [20].…”

Section: Resultsmentioning

confidence: 99%

Supporting agents in intelligent environments with protocol information

Felicíssimo¹,

Viterbo²,

Valente³

et al. 2008

4th International Conference on Intelligent Environments (IE 08)

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Open Multi-Agent Systems (MAS) can be extremely dynamic due to heterogeneous agents that migrate among them in order to obtain resources or services not found locally. In this scenario, it is not reasonable to expect that foreign agents know in advance all the information of the MAS in which they will execute. Thus, this paper proposes DynaCIP, our approach for continuously supporting mobile agents with contextual information and presents a prototype application. * Work partially funded by projects CNPq 55.2068/2002-0 (ESSMA), CNPq 47.9824/04-5 (Ed. Universal), CAPES/Cofecub 482/05/07 (EMACA) and CNPq individual grants

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Section: Resultsmentioning

confidence: 99%

Supporting agents in intelligent environments with protocol information

Felicíssimo¹,

Viterbo²,

Valente³

et al. 2008

4th International Conference on Intelligent Environments (IE 08)

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“…Such AmI systems may be composed of distributed heterogeneous learning assistant agents, which can both conflict and co-operate with each other but have no necessary central locus of control. Multiagent system paradigm is a convenient abstraction for designing such systems [18]. Agents are usually used for modeling and engineering complex articulated systems including AmI systems that assists learning [19].…”

Section: IImentioning

confidence: 99%

Sharing Biomedical Learning Knowledge for Social Ambient Intelligence

Mohammed¹,

Fiaidhi²,

Mohammed³

2009

JCP

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In this paper, we describe a Bio-SAmI system, which is a Biomedical learning system that is context- aware and responsive to mobile learners sharing information on a social network. Bio-SAmI is a Web 2.0 enabled system which employees Social Ambient Intelligence techniques. The Bio-SAmI infrastructure is based on the Actor model that treats mobile users or “actors" as the universal primitives of computation. The actor model is implemented using a combination of Java enabled APIs including SALSA, JADE, LEAP and tuPrologME. The implemented prototype enable learners to share biomedical information represented by the DICOM SR standard in relation to the notion of inflammation as well as to respond to variety of learning queries including classifying learning case studies, finding learning case studies, locating a FOAF learner and syndication and aggregation of learning case studies .

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Informing Regulatory Dynamics in Open MASs

Felicíssimo

Choren

Briot

et al.

Coordination, Organizations, Institutions, and Norms in Agent Systems II

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International audienceWe believe that, in the near future, all multi-agent systems (MASs) will be open, permitting agents to migrate among MASs to obtain resources or services not found locally. In this scenario, open MASs should be enhanced with norms for restricting agents’ actions and, thus, avoiding unexpected behavior. In this work, we present a case study where an open MAS is enhanced with contextual norms. Agents from this MAS are continuously supported with precise norm information, according to their contexts (implicit situational information) and, then, can make better decisions. Although the presented case study is simple, it clearly shows different levels of norm abstractions and how agents can be influenced by norms while acting in a regulated open MASs

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Dynamic Contextual Regulations in Open Multi-agent Systems

Cited by 4 publications

References 5 publications

Supporting agents in intelligent environments with protocol information

Supporting agents in intelligent environments with protocol information

Sharing Biomedical Learning Knowledge for Social Ambient Intelligence

Informing Regulatory Dynamics in Open MASs

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