Alle Publikationen
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2018
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(2018): Extension of grounding mechanism for abstract words: Computational methods insights. In: Artificial Intelligence Review 50 (3), S. 467-494. DOI: 10.1007/s10462-017-9608-9
DOI: https://doi.org/10.1007/s10462-017-9608-9 Abstract: The attempts to model cognitive phenomena effectively have split the research community in two paradigms: symbolic and connectionist. The extension of grounding phenomenon for abstract words is very important for social interactions of cognitive robots in real scenarios. This paper reviews the strength of symbolic and connectionist methods to address the abstract word grounding problem in cognitive robots. In particular, the presented work is focused on designing and simulating cognitive robotics model to achieve a grounding mechanism for abstract words by using the semantic network approach, as well as examining the utility of connectionist computation for the same problem. Two neuro-robotics models based on feed forward neural network and recurrent neural network are presented to see the pros and cons of connectionist approach. The simulation results and review of attributes of these methods reveal that the proposed symbolic model offers the solution to the problem of grounding abstract words with attributes like high data storage capacity with recall accuracy, structural integrity and temporal sequence handling. Whereas, connectionist computation based solutions give more natural solution to this problem with some shortcomings that include combinatorial ambiguity, low storage capacity and structural rigidity. The presented results are not only important for the advancement in communication system of cognitive robot, also provide evidence for embodied nature of abstract language. (PsycINFO Database Record (c) 2019 APA, all rights reserved)
2016
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(2016): A context-aware approach in realization of socially intelligent industrial robots. In: Robotics and Computer-Integrated Manufacturing 37, S. 79-89. DOI: 10.1016/j.rcim.2015.07.002
Abstract: Contemporary industrial environments are usually constrained or limited in order to fit a fast, cheap and non-error prone production. Human-like system capabilities are not generally desirable there. But, recent trends in industrial robotics demand robust, flexible and efficient robots with a certain level of autonomy. Therefore, new and different approaches and perspectives in designing of industrial facilities are required. This paper reveals how a context-based reasoning can be used to achieve an intelligent robot group behavior. In order to achieve adaptivity, self-recovery or scalability of the system, a COgnitive MOdel for the Robot group control (COMOR) is developed. COMOR can be understood as an interpreter used to transform high-level context to low-level data, allowing machines to make context-based decisions. COMOR has three main parts and relies on a simulated Social Capital phenomenon as a feature of people. The first part is used to collect significant information from the environment. The second part is used to provide a set of possible solutions respecting the semantic domain description. The last part of COMOR is used to provide a behavioral component ensuring an optimal solution to given environmental conditions.
Keywords: adaptivity, Cognitive robotics, COMOR, COMOR / Social Capital, Context-awareness, Kognitionswissenschaft/Social Sciences/Humanities, Mensch-Technik-Relationen (MTR), Ontology, Probabilistic graphical models, Realtechnik, Sozial Intelligente Agenten, Soziale Robotik, Soziosensitive Systeme, Technik, Ubiquitous computing 2008
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(2008) : Measuring human-robot team effectiveness to determine an appropriate autonomy level: 2008 IEEE International Conference on Robotics and Automation: 2008 IEEE International Conference on Robotics and Automation: Pasadena, CA, USA: IEEE, S. 2146-2151
DOI: https://doi.org/10.1109/ROBOT.2008.4543524 Abstract: This paper proposes a methodology to measure the effectiveness of a human-robot team as part of an adjustable autonomy system. The effectiveness measure is aimed at determining an appropriate autonomy level prior to the system’s deployment. Two competing goals need to be traded off: maximising robot performance while minimising the amount of human input. The relative importance of the two goals depend on the mission priorities and constraints which are taken into account. The proposed methodology is applied to a human-robot communication system developed for task- oriented information exchange. The robot uses a decision- theoretic framework to act autonomously and to decide when to request input from human operators. The latter is achieved by computing the value-of-information an operator is able to provide which is compared to the cost of obtaining the information. For our system, the cost parameter represents the autonomy level to be determined. We demonstrate how an appropriate autonomy level can be found experimentally using a navigation task. In our experiment, the robot navigates through a set of simulated worlds with human input being generated by a software component. The results are used to find appropriate autonomy levels for three example missions and a subsequent user study.
Keywords: Angemessen(heit) (von Technik), Anthropometry, Automation, Cognitive robotics, Communication systems, Costs, decision-theoretic framework, Human robot interaction, human-robot communication system, ieee xplore, man-machine systems, Measurement, Navigation, Robot sensing systems, Robotics, Robots, safety, software component, task-oriented information exchange -
(2008) : IslEnquirer: Social user model acquisition through network analysis and interactive learning: 2008 IEEE Spoken Language Technology Workshop: Goa, India: IEEE, S. 117-120
DOI: https://doi.org/10.1109/SLT.2008.4777854 Abstract: We present an approach to introduce social awareness in interactive systems. The IslEnquirer is a system which automatically builds social user models. It initializes the models by social network analysis of available offline data. These models are then verified and extended by interactive learning which is carried out by a robot initiated spoken dialog with the user.
Keywords: Automatic speech recognition, Automation, Cognitive robotics, Context modeling, Data Mining, Humanoid Robots, Humans, ieee xplore, Information retrieval, interactive learning, interactive system, interactive systems, IslEnquirer, Künstliche Intelligenz, learning (artificial intelligence), Robot, Robotics, social awareness, social network analysis, Social network services, social networking (online), social networks, social user model acquisition, spoken dialog, spoken dialog system, user modeling, user modelling 2006
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(2006) : Multimodal Human-Robot Interaction Framework for a Personal Robot: The 15th IEEE International Symposium on Robot and Human Interactive Communication, 2006: RO-MAN 2006 ; 6-8 Sept. 2006, University of Hertfordshire, Hatfield, United Kingdom ; proceedings: RO-MAN 2006: The 15th IEEE International Symposium on Robot and Human Interactive Communication: Hatfield: 9/6/2006 - 9/8/2006. IEEE International Symposium on Robot and Human Interactive Communication; IEEE Ro-Man: Piscataway, NJ: IEEE, S. 39-44
Abstract: This paper presents a framework for multimodal human-robot interaction. The proposed framework is being implemented in a personal robot called Maggie, developed at RoboticsLab of the University Carlos III of Madrid for social interaction research. The control architecture of this personal robot is a hybrid control architecture called AD (automatic-deliberative) that incorporates an emotion control system (ECS). Maggie's main goal is to interact establish a peer-to-peer relationship with humans. To achieve this goal, a set of human-robot interaction skills are developed based on the proposed framework. The human-robot interaction skills imply tactile, visual, remote voice and sound modes. The multi-modal fusion and synchronization are also presented in this paper.
2005
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(2005) : Fostering common ground in human-robot interaction: ROMAN 2005. IEEE International Workshop on Robot and Human Interactive Communication, 2005: ROMAN 2005. IEEE International Workshop on Robot and Human Interactive Communication, 2005: Nashville, TN, USA: IEEE, S. 729-734
DOI: https://doi.org/10.1109/ROMAN.2005.1513866 Abstract: Effective communication between people and interactive robots would benefit if they have a common ground of understanding. I discuss how the common ground principle of least collective effort can be used to predict and design human robot interactions. Social cues lead people to create a mental model of a robot and estimates of its knowledge. People’s mental model and knowledge estimate would, in turn, influence the effort they expend to communicate with the robot. People would explain their message in less detail to a knowledgeable robot with which they have more common ground. This process can be leveraged to design interactions that have an appropriate style of robot direction and that accommodate to differences among people.
Keywords: Airports, Angemessen(heit) (von Technik), Cognitive robotics, Cognitive Science, common ground principle, Communication effectiveness, Human robot interaction, Humanoid Robots, human-robot interaction, ieee xplore, interactive robots, knowledge estimation, mental model, Orbital robotics, Robot kinematics, Security, social robots, Testing, user interfaces 2004
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(2004) : Toward an actualization of social intelligence in human and robot collaborative systems: 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566), 4: 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566): Sendai, Japan: IEEE
DOI: https://doi.org/10.1109/IROS.2004.1389916 Abstract: As robot technology is evolving and creating a social community between humans and robots, it is necessary to research and develop a new type of intelligence, which we refer to as "social intelligence”. Social intelligence enables natural and socially appropriate interactions. Its importance is gaining a growing interest among not just the human-computer interaction researchers but also robot technology researchers and developers. This article discusses the definition, importance, and benefits of social intelligence in human and robot collaborative systems. The virtual social environment is employed to implement an experimental social intelligence system because of its low cost and high flexibility. Software robots (i.e. agents) with the social intelligence model have been implemented by featuring an emotion model and a personality model under the virtual environment. The social intelligence model that handles affective responses is based on the theories of personality, emotion, and human-media interaction such as cognitive appraisal theory and media equation. The experiment was conducted with the virtual learning collaborative system to examine the effect of the social intelligence model in the collaborative system. The data showed that the users had more positive impressions about the usefulness and the application and learning experience when the cooperative agent displayed some social responses with personality and emotions. It should be noted here that the cooperative agent did not provide any explicit assistance for the human user such as giving clues and showing answers, and yet the user’s evaluation on the usefulness of the learning system was influenced by the social agent. The data also suggested that the cooperative agent contributed to the effectiveness of the learning system.
Keywords: APPRAISAL, Artificial intelligence, Cognitive robotics, Collaboration, Costs, groupware, human collaborative system, Human Computer Interaction, Human robot interaction, Humanoid Robots, ieee xplore, intelligent agent, intelligent robots, Intelligent Systems, Künstliche Intelligenz, learning system, learning systems, robot collaborative system, robot technology, Social intelligence, Software agents, software robot, Virtual environment
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