Elder Rizzon Santos

Proposta Brasileira de Metadados para Objetos de Aprendizagem Baseados em Agentes (OBAA)

This paper presents the Agent Based Learning Objects - OBAA standard proposal. The main goal of the research was to establish a standardized specification of the technical and functional requirements of learning objects that should allow the interoperability of the contents between Web and Digital TV environment, supporting accessibility and pedagogical issues. In this proposal it has been explored the convergence among the multiagent systems, LO and ubiquitous computing technologies, allowing the authoring, storage and recovery of LO in varied contexts and through different digital platforms. The result of this research was the definition of the OBAA proposal containing the requirements, specifications and architectures that will compose the Brazilian standard for the management, transmission, storage, search, editing and use of LO that can be distributed and used in an integrated manner within the Web platforms, mobile devices and Digital TV.

Brazilian Proposal for Agent-Based Learning Objects Metadata Standard - OBAA

This paper presents the Agent Based Learning Objects - OBAA standard proposal. The main goal of the research was to establish a standardized specification of the technical and functional requirements of interoperable learning objects. In our context, interoperability regards the operation of the content inside Web, Digital TV and mobile environments, supporting accessibility and pedagogical issues. In this proposal it has been explored the convergence among the multi-agent systems, learning object and ubiquitous computing technologies, allowing the authoring, storage and recovery of learning object in varied contexts and through different digital platforms. The result of this research was the definition of the OBAA proposal containing the requirements, specifications and architectures that will compose the Brazilian standard for the management, transmission, storage, search, editing and use of interoperable learning object.

An ontology-based approach to interoperability for Bayesian agents

This paper presents an ontology-based approach to promote the interoperability among agents that represent their knowledge through Bayesian networks. This research relies on semantic web foundations to achieve knowledge interoperability in the context of multiagent systems. Our first step was the specification of an ontology that formalizes the structures of the Bayesian network representation. Once handled the issue of the knowledge representation, we specify how a Bayesian agent operates such representation. Thus, we define a model of internal architecture to support Bayesian agents in the knowledge sharing and maintenance tasks.

Projeto OBAA: Uma abordagem com objetos de aprendizagem interoperáveis baseados na web e na televisão digital

This article presents the studies conducted in the OBAA project - Agent Based Learning Object. Some defini- tions of learning objects, repositories, interactive digital te- levision, and multi-system agents are presented. Standards to build learning objects and programs for digital television were investigated and analyzed in order to specify a new multiplatform pattern, in which contents that can be used in web and digital television are built.

Interoperability for Bayesian agents in the semantic web

This paper presents an ontology-based approach to promote the interoperability among agents that represent their knowledge through Bayesian networks. This research relies on Semantic Web foundations to achieve knowledge interoperability in the context of multiagent systems. Our first step was the specification of an ontology that formalizes the structures of the Bayesian network representation. It was developed using OWL, which is a W3C recommendation for ontology language. Once handled the issue of the knowledge representation, we specify how a Bayesian agent operates such representation. Thus, we define a model of internal architecture to support Bayesian agents in the knowledge sharing and maintenance tasks. The utilization of the architecture is exemplified through a case study developed in the context of a multiagent educational portal (PortEdu). The case study demonstrates the interoperability resulted from the architecture integration with Bayesian agents hosted in PortEdu.

Interoperable Bayesian Agents for Collaborative Learning

This paper presents issues related to an educational portal design and collaboration in intelligent tutoring systems (ITS). In order to achieve the collaboration it was necessary to provide a way to interoperate knowledge among the heterogeneous systems. We have been developing ITS as resources to improve the individual and personalized learning. We believe that individual experiences can be more successful when the student has more autonomy and he is less dependent of the professor. This paper details the social agent, an agent to improve students learning stimulating his interaction with other students, specially how this agent exchange Bayesian knowledge among AMPLIA agents. The AMPLIA environment is an intelligent probabilistic multi-agent environment to support the diagnostic reasoning development and the diagnostic hypotheses modeling of domains with complex and uncertain knowledge, like medical area.

Interoperable Bayesian Agents for Collaborative Learning Environments

Collaborative work can be supported by many tools and it has been included in a large number of learning environments design. This paper presents issues related to an educational portal design and collaboration in Intelligent Tutoring Systems (ITS). In order to achieve the collaboration it was necessary to provide a way to interoperate knowledge among the heterogeneous systems. We have been developing ITS as resources to improve the individual and personalized learning. We believe that individual experiences can be more successful when the student has more autonomy and he is less dependent of the professor. In this research direction, this paper details the Social Agent reasoning, an agent to improve students learning stimulating his interaction with other students, and how this agent exchange bayesian knowledge among AMPLIA agents. The AMPLIA environment is an Intelligent Probabilistic Multi-agent Environment to support the diagnostic reasoning development and the diagnostic hypotheses modeling of domains with complex and uncertain knowledge, like medical area.