Developing Multi‐Agent Systems with JADE

Bellifemine, Fabio; Poggi, Agostino; Rimassa, Giovanni

doi:10.1002/9780470058411

Cited by 1,816 publications

(741 citation statements)

References 17 publications

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“…Desires represent the tasks allocated to the agent, this corresponds to the objective or goals the agent should accomplish which in effect causes a change in the future states or beliefs of the agents and their environment [6], [4]. Intentions represent desires that the agent is committed to achieving [4].…”

Section: Belief Desires and Intentionsmentioning

confidence: 99%

Computational Estimate Visualisation and Evaluation of Agent Classified Rules Learning System

Ehimwenma

Beer

Crowther

2016

Int. J. Emerg. Technol. Learn.

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Abstract!Student modelling and agent classified rules learning as applied in the development of the intelligent Preassessment System has been presented in [10], [11]. In this paper, we now demystify the theory behind the development of the pre-assessment system followed by some computational experimentation and graph visualisation of the agent classified rules learning algorithm in the estimation and prediction of classified rules. In addition, we present some preliminary results of the pre-assessment system evaluation. From the results, it is gathered that the system has performed according to its design specification.

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Section: Belief Desires and Intentionsmentioning

confidence: 99%

Computational Estimate Visualisation and Evaluation of Agent Classified Rules Learning System

Ehimwenma

Beer

Crowther

2016

Int. J. Emerg. Technol. Learn.

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“…MACRO is based on a hierarchical organization. Other example is presented in [24], where a work based on Situation Calculus that uses intelligent agents for dynamic environment monitoring such as battlefields or buildings is described. One of the common applications of multi-agent architectures is robot control, and the rolebased architecture is a common choice for this problem [25] [26] [27].…”

Section: Structural Health Monitoring (Shm)mentioning

confidence: 99%

Evaluation of Multi-agent Architecture for Structural Damage Detection and Location

et al. 2017

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Abstract-In this paper the results of using a Multi-agent system (MAS) for Structural Health Monitoring (SHM) are detailed. A study between different MAS architectures reported in literature is presented in order to select and adapt the most adequate one for SHM tasks. Requirements are established according to recent solutions, where main parameters are type and number of sensors and communication protocols, among others. MAS technique uses several intelligent agents, that are algorithms able to act in a reactive or active way. Their action depends on surrounding environment or collected data. These agents can work in a decentralized way, searching the fulfillment of an individual goal or they can work with another system to achieve a common goal. Decision is based on their internal state (beliefs, goals and commitments). MAS' effectiveness depends on the interconnection between the agents. Type of agents is defined according to its communication method and protocol, common and individual goals, among others. Decentralization and versatility are two important characteristics of MAS technique useful to solve SHM problem. This is one of the main motivations to consider this technique to be a good approach for the studied problem. A benchmark numerical model, which consists of a metallic framework, was used to validate and demonstrate the feasibility of the selected architecture for SHM.

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“…Java Agent Development Environment (JADE) is a middleware that facilitates the development of multi-agent systems. It provides a Foundation for Intelligent Physical Agents (FIPA) compliant environment and an implementation of Multi agent system, Bellifemine et al (2010).…”

Section: Agent Technology and Multi Agent Systemmentioning

confidence: 99%

An Agent Based Framework for Sentiment Classification of Online Reviews Using Ontology

Kalaivani¹,

Shunmuganathan²

2014

Journal of Computer Science

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In this study, we design and develop an agent based framework for sentiment classification of online reviews using ontology. The book review ranking is based on the sentiment classification result. We propose a novel approach with the help of the JADE platform to solve problems by non-visual automatic sentiment classification. The description of book reviews ranking are generated from the ontology based mapping. This approach employs the data extraction agent which is used to retrieve the books comments i.e., the user reviews from the specified blogs. The Second agent is the recommendation agent i.e., domain ontology is used for identifying domain related features in comments. The Third agent is feature selection agent in which XML document content is split into single sentence. Each word in the sentence is mapped with ontology. A Mapping process is used for identifying the domain related sentences in that context. These processes are used for ranking the book results based on customer reviews. The book review ranking system can be extended to other product-review easily.

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Developing Multi‐Agent Systems with JADE

Cited by 1,816 publications

References 17 publications

Computational Estimate Visualisation and Evaluation of Agent Classified Rules Learning System

Computational Estimate Visualisation and Evaluation of Agent Classified Rules Learning System

Evaluation of Multi-agent Architecture for Structural Damage Detection and Location

An Agent Based Framework for Sentiment Classification of Online Reviews Using Ontology

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