The Experts below are selected from a list of 264 Experts worldwide ranked by ideXlab platform
José Santos-victor - One of the best experts on this subject based on the ideXlab platform.
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Gaussian mixture models for affordance learning using Bayesian Networks
2010 IEEE RSJ International Conference on Intelligent Robots and Systems, 2010Co-Authors: Pedro Osório, Ruben Martinez-cantin, Alexandre Bernardino, José Santos-victorAbstract:Affordances are fundamental descriptors of relationships between actions, objects and effects. They provide the means whereby a robot can predict effects, recognize actions, select objects and plan its behavior according to desired goals. This paper approaches the problem of an Embodied Agent exploring the world and learning these affordances autonomously from its sensory experiences. Models exist for learning the structure and the parameters of a Bayesian Network encoding this knowledge. Although Bayesian Networks are capable of dealing with uncertainty and redundancy, previous work considered complete observability of the discrete sensory data, which may lead to hard errors in the presence of noise. In this paper we consider a probabilistic representation of the sensors by Gaussian Mixture Models (GMMs) and explicitly taking into account the probability distribution contained in each discrete affordance concept, which can lead to a more correct learning.
Santos-victor José - One of the best experts on this subject based on the ideXlab platform.
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Gaussian Mixture Models for Affordance Learning Using Bayesian Networks
IEEE, 2010Co-Authors: Osório Pedro, Bernardino Alexandre, Martínez-cantín Rubén, Santos-victor JoséAbstract:Proceedings of: IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems (IROS 2010), October 18-22, 2010, Taipe, TaiwanAffordances are fundamental descriptors of relationships between actions, objects and effects. They provide the means whereby a robot can predict effects, recognize actions, select objects and plan its behavior according to desired goals. This paper approaches the problem of an Embodied Agent exploring the world and learning these affordances autonomously from its sensory experiences. Models exist for learning the structure and the parameters of a Bayesian Network encoding this knowledge. Although Bayesian Networks are capable of dealing with uncertainty and redundancy, previous work considered complete observability of the discrete sensory data, which may lead to hard errors in the presence of noise. In this paper we consider a probabilistic representation of the sensors by Gaussian Mixture Models (GMMs) and explicitly taking into account the probability distribution contained in each discrete affordance concept, which can lead to a more correct learning.European Community's Seventh Framework ProgramThis work has been partly supported by PTDC/EEAACR/70174/2006, grant SFRH/BPD/48857/2008 from FCT and EU projects HANDLE and FIRST-MM
Yanghee Kim - One of the best experts on this subject based on the ideXlab platform.
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an Embodied Agent helps anxious students in mathematics learning
Educational Technology Research and Development, 2017Co-Authors: Yanghee Kim, Jeffrey L Thayne, Quan WeiAbstract:Mathematics anxiety is known to be detrimental to mathematics learning. This study explored if an Embodied Agent could be used to help alleviate student anxiety in classrooms. To examine this potential, Agent-guided algebra lessons were developed, in which an animated Agent was equipped with prescriptive instructional guidance and anxiety treating messages. The lessons were deployed in regular mathematics classrooms, one lesson per day over a week, with 138 boys and girls in the 9th grade in the United States. After taking the weeklong Agent-based lessons, students decreased in their mathematics anxiety (p = .042) and increased in mathematics learning (p = .001), regardless of the presence or absence of the Agents’ anxiety messages. The presence of the Agents’ messages only seemed to make a difference for high-anxiety students. This finding suggests that an Embodied Agent could provide affective support for students with special needs.
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gendered socialization with an Embodied Agent creating a social and affable mathematics learning environment for middle grade females
Journal of Educational Psychology, 2013Co-Authors: Yanghee Kim, Jae Hoon LimAbstract:This study examined whether or not Embodied-Agent-based learning would help middle-grade females have more positive mathematics learning experiences. The study used an explanatory mixed methods research design. First, a classroom-based experiment was conducted with one hundred twenty 9th graders learning introductory algebra (53% male and 47% female; 51% Caucasian and 49% Latino). The results revealed that learner gender was a significant factor in the learners’ evaluations of their Agent (η² = .07), the learners’ task-specific attitudes (η² = .05), and their task-specific self-efficacy (η² = .06). In-depth interviews were then conducted with 22 students selected from the experiment participants. The interviews revealed that Latina and Caucasian females built a different type of relationship with their Agent and reported more positive learning experiences as compared with Caucasian males. The females’ favorable view of the Agent-based learning was largely influenced by their everyday classroom experiences, implying that students’ learning experience in real and virtual spaces was interconnected.
Pedro Osório - One of the best experts on this subject based on the ideXlab platform.
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Gaussian mixture models for affordance learning using Bayesian Networks
2010 IEEE RSJ International Conference on Intelligent Robots and Systems, 2010Co-Authors: Pedro Osório, Ruben Martinez-cantin, Alexandre Bernardino, José Santos-victorAbstract:Affordances are fundamental descriptors of relationships between actions, objects and effects. They provide the means whereby a robot can predict effects, recognize actions, select objects and plan its behavior according to desired goals. This paper approaches the problem of an Embodied Agent exploring the world and learning these affordances autonomously from its sensory experiences. Models exist for learning the structure and the parameters of a Bayesian Network encoding this knowledge. Although Bayesian Networks are capable of dealing with uncertainty and redundancy, previous work considered complete observability of the discrete sensory data, which may lead to hard errors in the presence of noise. In this paper we consider a probabilistic representation of the sensors by Gaussian Mixture Models (GMMs) and explicitly taking into account the probability distribution contained in each discrete affordance concept, which can lead to a more correct learning.
Osório Pedro - One of the best experts on this subject based on the ideXlab platform.
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Gaussian Mixture Models for Affordance Learning Using Bayesian Networks
IEEE, 2010Co-Authors: Osório Pedro, Bernardino Alexandre, Martínez-cantín Rubén, Santos-victor JoséAbstract:Proceedings of: IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems (IROS 2010), October 18-22, 2010, Taipe, TaiwanAffordances are fundamental descriptors of relationships between actions, objects and effects. They provide the means whereby a robot can predict effects, recognize actions, select objects and plan its behavior according to desired goals. This paper approaches the problem of an Embodied Agent exploring the world and learning these affordances autonomously from its sensory experiences. Models exist for learning the structure and the parameters of a Bayesian Network encoding this knowledge. Although Bayesian Networks are capable of dealing with uncertainty and redundancy, previous work considered complete observability of the discrete sensory data, which may lead to hard errors in the presence of noise. In this paper we consider a probabilistic representation of the sensors by Gaussian Mixture Models (GMMs) and explicitly taking into account the probability distribution contained in each discrete affordance concept, which can lead to a more correct learning.European Community's Seventh Framework ProgramThis work has been partly supported by PTDC/EEAACR/70174/2006, grant SFRH/BPD/48857/2008 from FCT and EU projects HANDLE and FIRST-MM