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1
Dialogue patterns of an arabic robot receptionist
In: http://www.ri.cmu.edu/pub_files/2010/3/p167-makatchev.pdf (2010)
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2
Conceptual Spatial Representations for Indoor Mobile Robots
In: http://www.nada.kth.se/%7Epatric/publications/zender_etal08-ras-aam.pdf (2008)
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3
Timing of visual and spoken input in robot instructions
In: http://www.swrtec.de/swrtec/research/publications/WolfBugmannsubmissionv3.pdf (2006)
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4
Emotion and Sociable Humanoid Robots
In: http://web.media.mit.edu/~cynthiab/NewFiles/./Papers/Breazeal-ijhcs02-final.pdf (2002)
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5
Recognition of affective communicative intent in robot-directed speech
In: http://web.media.mit.edu/~cynthiab/NewFiles/./Papers/breazeal-aryananda-AutoRo02.pdf (2002)
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6
Non-instructional Linguistic Communication with Virtual Actors
In: http://www-scm.tees.ac.uk/users/f.charles/publications/conferences/2001/roman2001.pdf (2001)
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7
Genetic Learning of Fuzzy Reactive Controllers
In: http://gsyc.escet.urjc.es/robotica/ras98.pdf (1998)
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8
Abstract Genetic learning of fuzzy reactive controllers Robotics and Autonomous Systems
In: http://gsyc.es/~vmo/pubs/ras98.pdf (1998)
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9
A Learning Procedure for a Fuzzy System: Application to Obstacle Avoidance
In: ftp://lamiftp.epfl.ch/src/hg_isfl95.ps.Z (1995)
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10
Coupling Robot Perception and Online Simulation for Grounding Conversational Semantics
In: http://www.media.mit.edu/cogmac/publications/smc03final.pdf
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11
of meaning. Evolution of Communication, 4/2: 253-296. Meaning = Life (+ Culture):
In: http://www.mentaldev.org/JournalPapersPDF/Meaning=Life-offprint.pdf
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12
Knowledge Transfer Between Robots with Identical Tasks Execution
In: http://www.wseas.us/e-library/conferences/2006venice/papers/539-656.pdf
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13
Autonomous Robot Motion Planning in Diverse Terrain Using Genetic Algorithms
In: http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/62-fries.pdf
Abstract: Optimal motion planning is critical for the successful operation of an autonomous mobile robot. Many proposed approaches use either fuzzy logic or genetic algorithms (GAs), however, most approaches offer only path planning or only trajectory planning, but not both. In addition, few approaches attempt to address the impact of varying terrain conditions on the optimal path. This paper presents a fuzzy-genetic approach that provides both path and trajectory planning, and has the advantage of considering diverse terrain conditions when determining the optimal path. The terrain conditions are modeled using fuzzy linguistic variables to allow for the imprecision and uncertainty of the terrain data. Although a number of methods have been proposed using GAs, few are appropriate for a dynamic environment or provide response in real-time. The method proposed in this paper is robust, allowing the robot to adapt to dynamic conditions in the environment.
Keyword: and generation; and Search – plan execution; autonomous robots; Control Methods; design Keywords Genetic algorithms; formation; fuzzy sets; General Terms Algorithms; I.2.8 [Computing Methodologies; Methodologies; motion planning; Problem Solving; robot; Robotics – autonomous vehicles
URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.152.8858
http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/62-fries.pdf
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