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  <title>E-Archivo Community:</title>
  <link rel="alternate" href="http://hdl.handle.net/10016/6649" />
  <subtitle />
  <id>http://hdl.handle.net/10016/6649</id>
  <updated>2013-05-22T07:40:40Z</updated>
  <dc:date>2013-05-22T07:40:40Z</dc:date>
  <entry>
    <title>HTML5 support for an accessible user-video-interaction on the Web</title>
    <link rel="alternate" href="http://hdl.handle.net/10016/16250" />
    <author>
      <name>Moreno, Lourdes</name>
    </author>
    <author>
      <name>Martínez, Paloma</name>
    </author>
    <author>
      <name>Iglesias, Ana</name>
    </author>
    <author>
      <name>González, María</name>
    </author>
    <id>http://hdl.handle.net/10016/16250</id>
    <updated>2013-02-12T14:42:21Z</updated>
    <published>2010-12-31T23:00:00Z</published>
    <summary type="text">Title: HTML5 support for an accessible user-video-interaction on the Web
Author(s): Moreno, Lourdes; Martínez, Paloma; Iglesias, Ana; González, María
Abstract: Multimedia content covers the Web, and we should provide access to all people. For this reason, it is very important to take into account accessibility requirements in the player to avoid barriers and to ensure access to this multimedia content as well as their resources. One of the most frequent barriers is the technological obstacle: the necessity for the user to install the required plug-ins in to order to access video. The new standard HTML5 provides a solution to this problem. However, it does not fully support accessibility requirements of W3C standards, including WCAG and interaction requirement of UAAG. This paper introduces an overall study of this new standard in relation to accessibility requirements for the players as well as an accessible HTML5 Media Player.</summary>
    <dc:date>2010-12-31T23:00:00Z</dc:date>
  </entry>
  <entry>
    <title>An experience applying reinforcement learning in a web-based adaptive and intelligent educational system</title>
    <link rel="alternate" href="http://hdl.handle.net/10016/16247" />
    <author>
      <name>Iglesias, Ana</name>
    </author>
    <author>
      <name>Martínez, Paloma</name>
    </author>
    <author>
      <name>Fernández, Fernando</name>
    </author>
    <id>http://hdl.handle.net/10016/16247</id>
    <updated>2013-02-20T11:57:47Z</updated>
    <published>2003-02-28T23:00:00Z</published>
    <summary type="text">Title: An experience applying reinforcement learning in a web-based adaptive and intelligent educational system
Author(s): Iglesias, Ana; Martínez, Paloma; Fernández, Fernando
Abstract: The definition of effective pedagogical strategies for coaching and tutoring students according to their needs is one of the most important issues in Adaptive and Intelligent Educational Systems (AIES). The use of a Reinforcement Learning (RL) model allows the system to learn automatically how to teach to each student individually, only based on the acquired experience with other learners with similar characteristics, like a human tutor does. The application of this artificial intelligence technique, RL, avoids to define the teaching strategies by learning action policies that define what, when and how to teach. In this paper we study the performance of the RL model in a DataBase Design (DBD) AIES, where this performance is measured on number of students required to acquire efficient teaching strategies.</summary>
    <dc:date>2003-02-28T23:00:00Z</dc:date>
  </entry>
  <entry>
    <title>ICAPS 2012. Proceedings of the third Workshop on the International Planning Competition</title>
    <link rel="alternate" href="http://hdl.handle.net/10016/14914" />
    <author>
      <name />
    </author>
    <id>http://hdl.handle.net/10016/14914</id>
    <updated>2012-07-16T22:19:46Z</updated>
    <published>2011-12-31T23:00:00Z</published>
    <summary type="text">Title: ICAPS 2012. Proceedings of the third Workshop on the International Planning Competition
Editors: Coles, Amanda; Coles, Andrew; García Olaya, Ángel; Jiménez, Sergio; Linares López, Carlos; Sanner, Scott
Description: 22nd International Conference on Automated Planning and Scheduling. June 25-29, 2012, Atibaia, Sao Paulo (Brazil). Proceedings of the 3rd the International Planning Competition</summary>
    <dc:date>2011-12-31T23:00:00Z</dc:date>
  </entry>
  <entry>
    <title>Learning in Large Cooperative Multi-Robot Domains</title>
    <link rel="alternate" href="http://hdl.handle.net/10016/13273" />
    <author>
      <name>Fernández, Fernando</name>
    </author>
    <author>
      <name>Parker, Lynne E.</name>
    </author>
    <id>http://hdl.handle.net/10016/13273</id>
    <updated>2012-02-08T08:40:42Z</updated>
    <published>2000-12-31T23:00:00Z</published>
    <summary type="text">Title: Learning in Large Cooperative Multi-Robot Domains
Author(s): Fernández, Fernando; Parker, Lynne E.
Abstract: The development of mechanisms that enable robot teams to autonomously generate cooperative behaviours is one of the most interesting issues in dis- tributed and autonomous robotic systems. In this paper, the application of reinforcement learning techniques to robot teams is studied, enabling the robot to learn cooperative behaviours based only on local information.</summary>
    <dc:date>2000-12-31T23:00:00Z</dc:date>
  </entry>
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