An Agent-Based Architecture for Virtual Environments for Training

2005 ◽  
pp. 212-234 ◽  
Author(s):  
Angélica de Antonio ◽  
Jaime Ramirez ◽  
Gonzalo Mendez
Keyword(s):  
Data Structure ◽  
Virtual Environments ◽  
System Architecture ◽  
New World ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Software Agents ◽  
Collaborative Development ◽  
Agent Based ◽  
Design And Implementation

This chapter proposes an architecture for the development of intelligent virtual environments for training (IVETs) which is based on a collection of cooperative software agents. The first level of the architecture is defined as an extension of the classical intelligent tutoring system architecture that adds a new world module. Several software agents are then identified within each module. They communicate among them directly via messages and indirectly via a common data structure that is used for the collaborative development of plans. Some details are provided about the most remarkable interactions that will be established among agents during the system’s execution. The proposed architecture, and its realization in a platform of generic and configurable agents, will facilitate the design and implementation of new IVETs, maximizing the reuse of existing components and the extensibility of the system to add new functionalities.

An Agent-Based Architecture for Virtual Environments for Training

2008 ◽  
pp. 326-341
Author(s):  
Angélica de Antonio ◽  
Jaime Ramirez ◽  
Gonzalo Mendez
Keyword(s):  
Data Structure ◽  
Virtual Environments ◽  
System Architecture ◽  
New World ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Software Agents ◽  
Collaborative Development ◽  
Agent Based ◽  
Design And Implementation

This chapter proposes an architecture for the development of intelligent virtual environments for training (IVETs) which is based on a collection of cooperative software agents. The first level of the architecture is defined as an extension of the classical intelligent tutoring system architecture that adds a new world module. Several software agents are then identified within each module. They communicate among them directly via messages and indirectly via a common data structure that is used for the collaborative development of plans. Some details are provided about the most remarkable interactions that will be established among agents during the system’s execution. The proposed architecture, and its realization in a platform of generic and configurable agents, will facilitate the design and implementation of new IVETs, maximizing the reuse of existing components and the extensibility of the system to add new functionalities.

Agent-Based Distributed Intelligent Tutoring System Using Case-Based Reasoning

2018 ◽  
pp. 901-928
Author(s):  
Shweta ◽  
Praveen Dhyani ◽  
O. P. Rishi
Keyword(s):  
Intelligent System ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Domain Model ◽  
Student Modeling ◽  
Specific Situation ◽  
Pedagogical Strategies ◽  
Tutoring Systems ◽  
Agent Based ◽  
Case Based

Intelligent Tutoring Systems have proven their worth in multiple ways and in multiple domains in education. In this chapter, the proposed Agent-Based Distributed ITS using CBR for enhancing the intelligent learning environment is introduced. The general architecture of the ABDITS is formed by the three components that generally characterize an ITS: the Student Model, the Domain Model, and the Pedagogical Model. In addition, a Tutor Model has been added to the ITS, which provides the functionality that the teacher of the system needs. Pedagogical strategies are stored in cases, each dictating, given a specific situation, which tutoring action to make next. Reinforcement learning is used to improve various aspects of the CBR module: cases are learned and retrieval and adaptation are improved, thus modifying the pedagogical strategies based on empirical feedback on each tutoring session. The student modeling is a core component in the development of proposed ITS. In this chapter, the authors describe how a Multi-Agent Intelligent system can provide effective learning using Case-Based Student Modeling.

Social and Affective Agents to Motivate Collaboration on Agent-Based Intelligent Tutoring Systems

2008 ◽  
pp. 201-227
Author(s):  
Elisa Boff ◽  
Cecília Dias Flores
Keyword(s):  
Learning Style ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Affective State ◽  
Educational Systems ◽  
Probabilistic Networks ◽  
Tutoring Systems ◽  
Agent Based ◽  
Social Agent ◽  
The Social

This chapter presents a social and affective agent, named social agent, that has been modeled using probabilistic networks in order to support and motivate collaboration in an intelligent tutoring system (ITS). The social agent suggests to students a workgroup to join in. Our testbed ITS is called AMPLIA, a probabilistic multiagent environment to support the diagnostic reasoning development and the diagnostic hypotheses modeling of domains with complex and uncertain knowledge, as the medical area. The AMPLIA environment is one of the educational systems, integrated in Portedu, which is a Web portal that provides access to educational contents and systems. The social agent belongs to Portedu platform and it is used by AMPLIA. The social agent reasoning is based on individual aspects, such as learning style, performance, affective state, personality traits, and group aspects, as acceptance and social skills. The chapter also presents some experiments using AMPLIA, and results obtained by the social agent.

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