145,640 research outputs found
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
Information fusion for context awareness in intelligent environments
The development of intelligent environments requires handling of data
perceived from users, received from environments and gathered from objects.
Such data is often used to implement machine learning tasks in order to predict
actions or to anticipate needs and wills, as well as to provide additional context
in applications. Thus, it is often needed to perform operations upon collected
data, such as pre-processing, information fusion of sensor data, and manage
models from machine learning. These machine learning models may have
impact on the performance of platforms and systems used to obtain intelligent
environments. In this paper, it is addressed the issue of the development of
middleware for intelligent systems, using techniques from information fusion
and machine learning that provide context awareness and reduce the impact of
information acquisition on both storage and energy efficiency. This discussion
is presented in the context of PHESS, a project to ensure energetic sustainability,
based on intelligent agents and multi-agent systems, where these techniques
are applied
Learning to Generate Unambiguous Spatial Referring Expressions for Real-World Environments
Referring to objects in a natural and unambiguous manner is crucial for
effective human-robot interaction. Previous research on learning-based
referring expressions has focused primarily on comprehension tasks, while
generating referring expressions is still mostly limited to rule-based methods.
In this work, we propose a two-stage approach that relies on deep learning for
estimating spatial relations to describe an object naturally and unambiguously
with a referring expression. We compare our method to the state of the art
algorithm in ambiguous environments (e.g., environments that include very
similar objects with similar relationships). We show that our method generates
referring expressions that people find to be more accurate (30% better)
and would prefer to use (32% more often).Comment: International Conference on Intelligent Robots and Systems (IROS
2019), Demo 1: Finding the described object (https://youtu.be/BE6-F6chW0w),
Demo 2: Referring to the pointed object (https://youtu.be/nmmv6JUpy8M),
Supplementary Video (https://youtu.be/sFjBa_MHS98
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
Resolving the Problem of Intelligent Learning Content in Learning Management Systems
Current e-learning standardization initiatives have put much effort into easing interoperability between systems and the reusability of contents. For this to be possible, one of the most relevant areas is the definition of a run-time environment, which allows Learning Management Systems to launch, track and communicate with learning objects. However, when dealing with intelligent content, these environments show important restrictions. In this article, we study these restrictions, comparing several standardized run-time environments with nonstandardized solutions that aim to overcome these constraints
An approach to develop intelligent learning environments by means of immersive virtual worlds
Merging Immersive Virtual Environments, Natural Language Processing and Artificial Intelligence techniques provides a number of advantages to develop Intelligent Environments for multiple applications. This paper is focused on the application of these technologies to develop intelligent learning environments. Education is one of the most interesting applications of immersive virtual environments, as their flexibility can be exploited in order to create heterogeneous groups from all over the world who can collaborate synchronously in different virtual spaces. We highlight the potential of virtual worlds as an educative tool and propose a model to create learning environments within Second Life or OpenSimulator combining the Moodle learning management system, embodied conversational metabots, and programmable 3D objects. Our proposal has been applied in several subjects of the Computer Science degree in the Carlos III University of Madrid. The results of the evaluation show that developed learning environment fosters engagement and collaboration and helps students to better understand complex concepts.This work was supported in part by Projects MINECO TEC2012-37832-C02-01, CICYT TEC2011-28626-C02-02, CAM CONTEXTS (S2009/TIC-1485).Publicad
An approach to develop intelligent learning environments by means of immersive virtual worlds
Merging Immersive Virtual Environments, Natural Language Processing and Artificial Intelligence techniques provides
a number of advantages to develop Intelligent Environments for multiple applications. This paper is focused on the application
of these technologies to develop intelligent learning environments. Education is one of the most interesting applications
of immersive virtual environments, as their flexibility can be exploited in order to create heterogeneous groups from all over the
world who can collaborate synchronously in different virtual spaces. We highlight the potential of virtual worlds as an educative
tool and propose a model to create learning environments within Second Life or OpenSimulator combining the Moodle learning
management system, embodied conversational metabots, and programmable 3D objects. Our proposal has been applied in
several subjects of the Computer Science degree in the Carlos III University of Madrid. The results of the evaluation show that
developed learning environment fosters engagement and collaboration and helps students to better understand complex concepts.Spanish Government TEC2012-37832-C02-01Consejo Interinstitucional de Ciencia y Tecnologia (CICYT) TEC2011-28626-C02-02Project CAM CONTEXTS S2009/TIC-148
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
Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learning
Skilled robotic manipulation benefits from complex synergies between
non-prehensile (e.g. pushing) and prehensile (e.g. grasping) actions: pushing
can help rearrange cluttered objects to make space for arms and fingers;
likewise, grasping can help displace objects to make pushing movements more
precise and collision-free. In this work, we demonstrate that it is possible to
discover and learn these synergies from scratch through model-free deep
reinforcement learning. Our method involves training two fully convolutional
networks that map from visual observations to actions: one infers the utility
of pushes for a dense pixel-wise sampling of end effector orientations and
locations, while the other does the same for grasping. Both networks are
trained jointly in a Q-learning framework and are entirely self-supervised by
trial and error, where rewards are provided from successful grasps. In this
way, our policy learns pushing motions that enable future grasps, while
learning grasps that can leverage past pushes. During picking experiments in
both simulation and real-world scenarios, we find that our system quickly
learns complex behaviors amid challenging cases of clutter, and achieves better
grasping success rates and picking efficiencies than baseline alternatives
after only a few hours of training. We further demonstrate that our method is
capable of generalizing to novel objects. Qualitative results (videos), code,
pre-trained models, and simulation environments are available at
http://vpg.cs.princeton.eduComment: To appear at the International Conference On Intelligent Robots and
Systems (IROS) 2018. Project webpage: http://vpg.cs.princeton.edu Summary
video: https://youtu.be/-OkyX7Zlhi
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