Improving interoperability among learning objects using FIPA agent communication framework

The reusability of learning material is based on three main features: modularity, discoverability and interoperability. Several researchers on Intelligent Learning Environments have proposed the use of architectures based on agent societies. Learning systems based on Multi-Agent architectures support the development of more interactive and adaptable systems and the Learning Objects approach gives reusability. We proposed an approach where learning objects are built based on agent architectures. This paper discusses how the Intelligent Learning Objects approach can be used to improve the interoperability between learning objects and pedagogical agents.Applications in Artificial Intelligence - AgentsRed de Universidades con Carreras en Informática (RedUNCI

A structured model metametadata technique to enhance semantic searching in metadata repository

This paper discusses on a novel technique for semantic searching and retrieval of information about learning materials. A novel structured metametadata model has been created to provide the foundation for a semantic search engine to extract, match and map queries to retrieve relevant results. Metametadata encapsulate metadata instances by using the properties and attributes provided by ontologies rather than describing learning objects. The use of ontological views assists the pedagogical content of metadata extracted from learning objects by using the control vocabularies as identified from the metametadata taxonomy. The use of metametadata (based on the metametadata taxonomy) supported by the ontologies have contributed towards a novel semantic searching mechanism. This research has presented a metametadata model for identifying semantics and describing learning objects in finer-grain detail that allows for intelligent and smart retrieval by automated search and retrieval software

Intelligent learning objects: an agent aproach to create interoperable learning objects

Reusing learning material is very important to design learning environments for real-life learning. The reusability of learning objects results from the product of three main features: modularity, discoverability and interoperability. We proposed learning objects built based on agent architectures, called Intelligent Learning Objects (ILO). This paper discusses how the ILO approach can be used to improve the interoperability among learning objects, learning menagement systems (LMS) and pedagogical agents.Education for the 21 st century - impact of ICT and Digital Resources ConferenceRed de Universidades con Carreras en Informática (RedUNCI

AI2-THOR: An Interactive 3D Environment for Visual AI

We introduce The House Of inteRactions (THOR), a framework for visual AI research, available at http://ai2thor.allenai.org. AI2-THOR consists of near photo-realistic 3D indoor scenes, where AI agents can navigate in the scenes and interact with objects to perform tasks. AI2-THOR enables research in many different domains including but not limited to deep reinforcement learning, imitation learning, learning by interaction, planning, visual question answering, unsupervised representation learning, object detection and segmentation, and learning models of cognition. The goal of AI2-THOR is to facilitate building visually intelligent models and push the research forward in this domain

Formalization of higher-level intelligence through integration of intelligent tutoring tools : a thesis presented in partial fulfilment of the requirements for the degree of Master of Information Systems, Department of Information Systems, Massey University, Palmerston North, New Zealand

In contrast with a traditional Intelligent Tutoring System (ITS), which attempts to be fairly comprehensive and covers enormous chunks of a discipline's subject matter, a basic Intelligent Tutoring Tool (ITT) (Patel & Kinshuk, 1997) has a narrow focus. It focuses on a single topic or a very small cluster of related topics. An ITT is regarded as a building block of a larger and more comprehensive tutoring system, which is fundamentally similar with the emerging technology "Learning Objects" (LOs) (LTSC, 2000a). While an individual ITT or LO focuses on a single topic or a very small cluster of knowledge, the importance of the automatic integration of interrelated ITTs or LOs is very clear. This integration can extend the scope of an individual ITT or LO, it can guide the user from a simple working model to a complex working model and provide the learner with a rich learning experience, which results in a higher level of learning. This study reviews and analyses the Learning Objects technology, as well as its advantages and difficulties. Especially, the LOs integration mechanisms applied in the existing learning systems are discussed in detail. As a result, a new ITT integration framework is proposed which extends and formalizes the former ITT integration structures (Kinshuk & Patel, 1997, Kinshuk, et al. 2003) in two ways: identifying and organizing ITTs, and describing and networking ITTs. The proposed ITTs integration framework has the following four notions: (1) Ontology, to set up an explicit conceptualisation in a particular domain, (2) Object Design and Sequence Theory, to identify and arrange learning objects in a pedagogical way through the processes of decomposing principled skills, synthesising working models and placing these models on scales of increasing complexity, (3) Metadata, to describe the identified ITTs and their interrelationships in a cross-platform XML format, and (4) Integration Mechanism, to detect and activate the contextual relationship

E-Learning and Intelligent Planning: Improving Content Personalization

Combining learning objects is a challenging topic because of its direct application to curriculum generation, tailored to the students' profiles and preferences. Intelligent planning allows us to adapt learning routes (i.e. sequences of learning objects), thus highly improving the personalization of contents, the pedagogical requirements and specific necessities of each student. This paper presents a general and effective approach to extract metadata information from the e-learning contents, a form of reusable learning objects, to generate a planning domain in a simple, automated way. Such a domain is used by an intelligent planner that provides an integrated recommendation system, which adapts, stores and reuses the best learning routes according to the students' profiles and course objectives. If any inconsistency happens during the route execution, e.g. the student fails to pass an assessment test which prevents him/her from continuing the natural course of the route, the systeGarrido, A.; Morales, L. (2014). E-Learning and Intelligent Planning: Improving Content Personalization. IEEE Revista Iberoamericana de Tecnologías del Aprendizaje. 9(1):1-7. doi:10.1109/RITA.2014.2301886S179

The OBAA Standard for Developing Repositories of Learning Objects: the Case of Ocean Literacy in Azores

This paper describes the existing web resources of learning objects to promote ocean literacy. The several projects and sites are explored, and the shortcomings revealed. The limitations identified include insufficient metadata about registered learning objects and lack of support for intelligent applications. As solution, we promote the seaThings project that relies on a multi-disciplinary approach to promote literacy in the marine environment by implementing a specific Learning Objects repositories (LOR) and a federation of repositories (FED), supported by a OBAA, a versatile and innovative standard that will provide the necessary support for intelligent applications for education purposes, to be used in schools and other educational institutions.info:eu-repo/semantics/publishedVersio

Research on knowledge representation, machine learning, and knowledge acquisition

Research in knowledge representation, machine learning, and knowledge acquisition performed at Knowledge Systems Lab. is summarized. The major goal of the research was to develop flexible, effective methods for representing the qualitative knowledge necessary for solving large problems that require symbolic reasoning as well as numerical computation. The research focused on integrating different representation methods to describe different kinds of knowledge more effectively than any one method can alone. In particular, emphasis was placed on representing and using spatial information about three dimensional objects and constraints on the arrangement of these objects in space. Another major theme is the development of robust machine learning programs that can be integrated with a variety of intelligent systems. To achieve this goal, learning methods were designed, implemented and experimented within several different problem solving environments