145,836 research outputs found
Is a Semantic Web Agent a Knowledge-Savvy Agent?
The issue of knowledge sharing has permeated the field of distributed AI and in particular, its successor, multiagent systems. Through the years, many research and engineering efforts have tackled the problem of encoding and sharing knowledge without the need for a single, centralized knowledge base. However, the emergence of modern computing paradigms such as distributed, open systems have highlighted the importance of sharing distributed and heterogeneous knowledge at a larger scaleāpossibly at the scale of the Internet. The very characteristics that define the Semantic Webāthat is, dynamic, distributed, incomplete, and uncertain knowledgeāsuggest the need for autonomy in distributed software systems. Semantic Web research promises more than mere management of ontologies and data through the definition of machine-understandable languages. The openness and decentralization introduced by multiagent systems and service-oriented architectures give rise to new knowledge management models, for which we canāt make a priori assumptions about the type of interaction an agent or a service may be engaged in, and likewise about the message protocols and vocabulary used. We therefore discuss the problem of knowledge management for open multi-agent systems, and highlight a number of challenges relating to the exchange and evolution of knowledge in open environments, which pertinent to both the Semantic Web and Multi Agent System communities alike
Designing Personalized Online Learning Environments for Adult Learners
2002Customizing instruction to meet individual needs is one of the foundational cornerstones of
today's learner-centered paradigms. Adult learners have a wide range of differences in their
backgrounds, interests, abilities, and learning styles; instruction, therefore, needs to be
designed in such a way as to meet the highly diverse needs of adult education settings. The
World Wide Web presents enormous potential for providing a technological environment for
the optimal delivery of personalized instruction for individual learners. It is argued, however,
that existing Web-based instruction fails to effectively customize instructions for individual
learners. Therefore, we are in need of an ongoing refinement and creativity in our generation
and treatment of theories of instruction geared towards the generation of personalized
learning environments. Here, the attempt is made to develop an instructional-design theory
for personalized online learning for online adult learners, with a special focus on the question
of solving ill-structured problems. Theory, on a general level, is discussed insofar as it has
emerged from the goals, preconditions, and underlying values, with an eye to the methods of
instruction that are optimal for achieving the goals. The methods of instruction in this
instructional-design theory are composed of four major components (goal-setting activities,
engaging in the learning task, performing the task, and reviewing and reflecting upon the
output of the task), with some corollaries detailing each major component. These methods
incorporate the use of such Web technology as the learner management system, the learning
objects system, and pedagogical agents to foster personalized learning process in online
learning environments
Structuring visual exploratory analysis of skill demand
The analysis of increasingly large and diverse data for meaningful interpretation and question answering is handicapped by human cognitive limitations. Consequently, semi-automatic abstraction of complex data within structured information spaces becomes increasingly important, if its knowledge content is to support intuitive, exploratory discovery. Exploration of skill demand is an area where regularly updated, multi-dimensional data may be exploited to assess capability within the workforce to manage the demands of the modern, technology- and data-driven economy. The knowledge derived may be employed by skilled practitioners in defining career pathways, to identify where, when and how to update their skillsets in line with advancing technology and changing work demands. This same knowledge may also be used to identify the combination of skills essential in recruiting for new roles. To address the challenges inherent in exploring the complex, heterogeneous, dynamic data that feeds into such applications, we investigate the use of an ontology to guide structuring of the information space, to allow individuals and institutions to interactively explore and interpret the dynamic skill demand landscape for their specific needs. As a test case we consider the relatively new and highly dynamic field of Data Science, where insightful, exploratory data analysis and knowledge discovery are critical. We employ context-driven and task-centred scenarios to explore our research questions and guide iterative design, development and formative evaluation of our ontology-driven, visual exploratory discovery and analysis approach, to measure where it adds value to usersā analytical activity. Our findings reinforce the potential in our approach, and point us to future paths to build on
Constructing a Virtual Training Laboratory Using Intelligent Agents
This paper reports on the results and experiences of the Trilogy project; a collaborative project concerned with the development of a virtual research laboratory using intelligence agents. This laboratory is designed to support the training of research students in telecommunications traffic engineering. Training research students involves a number of basic activities. They may seek guidance from, or exchange ideas with, more experienced colleagues. High quality academic papers, books and research reports provide a sound basis for developing and maintaining a good understanding of an area of research. Experimental tools enable new ideas to be evaluated, and hypotheses tested. These three components-collaboration, information and experimentation- are central to any research activity, and a good training environment for research should integrate them in a seamless fashion. To this end, we describe the design and implementation of an agent-based virtual laboratory
A Semantic Web of Know-How: Linked Data for Community-Centric Tasks
This paper proposes a novel framework for representing community know-how on
the Semantic Web. Procedural knowledge generated by web communities typically
takes the form of natural language instructions or videos and is largely
unstructured. The absence of semantic structure impedes the deployment of many
useful applications, in particular the ability to discover and integrate
know-how automatically. We discuss the characteristics of community know-how
and argue that existing knowledge representation frameworks fail to represent
it adequately. We present a novel framework for representing the semantic
structure of community know-how and demonstrate the feasibility of our approach
by providing a concrete implementation which includes a method for
automatically acquiring procedural knowledge for real-world tasks.Comment: 6th International Workshop on Web Intelligence & Communities (WIC14),
Proceedings of the companion publication of the 23rd International Conference
on World Wide Web (WWW 2014
Towards a Framework for Developing Mobile Agents for Managing Distributed Information Resources
Distributed information management tools allow users to author, disseminate, discover and manage information within large-scale networked environments, such as the Internet. Agent technology provides the flexibility and scalability necessary to develop such distributed information management applications. We present a layered organisation that is shared by the specific applications that we build. Within this organisation we describe an architecture where mobile agents can move across distributed environments, integrate with local resources and other mobile agents, and communicate their results back to the user
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