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358 results on '"Volz, Richard A."'

11. Forming Proactive Team Cooperation by Observations

Author

Zhang, Yu, Volz, Richard A., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Khosla, Rajiv, editor, Howlett, Robert J., editor, and Jain, Lakhmi C., editor

Published
2005
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12. The Semantics of MALLET–An Agent Teamwork Encoding Language

Author

Fan, Xiaocong, Yen, John, Miller, Michael S., Volz, Richard A., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Leite, João, editor, Omicini, Andrea, editor, Torroni, Paolo, editor, and Yolum, pInar, editor

Published
2005
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14. Proactive Communications in Agent Teamwork

Author

Yen, John, Fan, Xiaocong, Volz, Richard A., Goos, Gerhard, editor, Hartmanis, Juris, editor, van Leeuwen, Jan, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, and Dignum, Frank, editor

Published
2004
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16. Training Teams with Collaborative Agents

Author

Miller, Michael S., Yin, Jianwen, Volz, Richard A., Ioerger, Thomas R., Yen, John, Goos, Gerhard, editor, Hartmanis, Juris, editor, van Leeuwen, Jan, editor, Gauthier, Gilles, editor, Frasson, Claude, editor, and VanLehn, Kurt, editor

Published
2000
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20. COINGRAD; Control Oriented Interactive Graphical Analysis and Design.

Author

Michigan Univ., Ann Arbor. Dept. of Electrical and Computer Engineering. and Volz, Richard A.

Abstract

The computer is currently a vital tool in engineering analysis and design. With the introduction of moderately priced graphics terminals, it will become even more important in the future as rapid graphic interaction between the engineer and the computer becomes more feasible in computer-aided design (CAD). To provide a vehicle for introducing undergraduate engineering students to the applications of CAD in the automatic control area, a series of three programs--named Control Oriented Interactive Graphical Analysis and Design (COINGRAD)--has been written. These incorporate the basic calculations in this area, including time response, frequency response, and root locus; in addition, a limited optimization facility is included with the time and frequency response programs. The major features of the programs include graphic interactive capability and ease of operation for the user. (Author)

Published
1974

21. Computer Instructional Aids for Undergraduate Control Education.

Author

Michigan Univ., Ann Arbor. Dept. of Electrical and Computer Engineering. and Volz, Richard A.

Abstract

Engineering is coming to rely more and more heavily upon the computer for computations, analyses, and graphic displays which aid the design process. A general purpose simulation system, the Time-shared Automatic Control Laboratory (TACL), and a set of computer-aided design programs, Control Oriented Interactive Graphic Analysis and Design (COINGRAD), are being used to introduce undergraduate engineering students to the power and utility of computer-aided tools for automatic control. TACL and COINGRAD simulate time responses for physical systems, provide standard analysis and design calculators, and introduce students to optimization in design. They are simple and user-oriented, have interactive and graphic display capabilities, and employ a natural mathematical language. Since these programs perform the basic calculations required for classical control system analysis, they free the student from the repeated enumeration of system behavior and allow him to concentrate upon the underlying principles involved. (PB)

Published
1974

28. A microRNA allele that emerged prior to apple domestication may underlie fruit size evolution

Author

Yao, Jia-Long, Xu, Juan, Cornille, Amandine, Tomes, Sumathi, Karunairetnam, Sakuntala, Luo, Zhiwei, Bassett, Heather, Whitworth, Claire, Rees-George, Jonathan, Ranatunga, Chandra, Snirc, Alodie, Crowhurst, Ross, de Silva, Nihal, Warren, Ben, Deng, Cecilia, Kumar, Satish, Chagné, David, Bus, Vincent G. M., Volz, Richard K., Rikkerink, Erik H. A., Gardiner, Susan E., Giraud, Tatiana, MacDiarmid, Robin, and Gleave, Andrew P.

Published
2015
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37. MALLET--a Multi-Agent Logic Language for Encoding teamwork

Author

Fan, Xiaocong, Yen, John, Miller, Michael, Ioerger, Thomas R., and Volz, Richard

Subjects
Artificial intelligence -- Analysis, Work groups, Artificial intelligence, Business, Computers, Electronics, Electronics and electrical industries
Abstract

MALLET, a Multi-Agent Logic Language for Encoding Teamwork, is intended to enable expression of teamwork emulating human teamwork, allowing experimentation with different levels and forms of inferred team intelligence. A consequence of this goal is that the actual teamwork behavior is determined by the level of intelligence built into the underlying system as well as the semantics of the language. In this paper, we give the design objectives, the syntax, and an operational semantics for MALLET in terms of a transition system. We show how the semantics can be used to reason about the behaviors of team-based agents. The semantics can also be used to guide the implementation of various MALLET interpreters emulating different forms of team intelligence, as well as formally study the properties of team-based agents specified in MALLET. We have explored various forms of proactive information exchange behavior embodied in human teamwork using the CAST system, which implements a built-in MALLET interpreter. Index Terms--Artificial intelligence, distributed artificial intelligence, languages and structures.

Published
2006

39. Development of a distributed multi-player computer game for scientific experimentation of team training protocols

Author

Cao, Sen, Volz, Richard A., Johnson, Jamison, Nanjanath, Maitreyi, Whetzel, Jonathan, and Xu, Dianxiang

Subjects
Artificial intelligence -- Usage, Computer supported collaborative learning, Artificial intelligence, Business, Library and information science
Abstract

Use of intelligence agents for training teams is examined with reference to Space Fortress video game.

Published
2004

40. Red to Brown: An Elevated Anthocyanic Response in Apple Drives Ethylene to Advance Maturity and Fruit Flesh Browning

Author

Espley, Richard V., primary, Leif, Davin, additional, Plunkett, Blue, additional, McGhie, Tony, additional, Henry-Kirk, Rebecca, additional, Hall, Miriam, additional, Johnston, Jason W., additional, Punter, Matthew P., additional, Boldingh, Helen, additional, Nardozza, Simona, additional, Volz, Richard K., additional, O’Donnell, Samuel, additional, and Allan, Andrew C., additional

Published
2019
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41. Generalized Recognition of Single-Ended Contact Formations

Author

Everett, Louis J., Ravuri, Rakesh, Volz, Richard A., and Skubic, Marjorie

Subjects
Robotics -- Research, Kinematics -- Research, Automation -- Research, Mobile robots -- Research
Abstract

Contact formations have proven useful for programming robots by demonstration for operations involving contact. These techniques require real time recognition of contact formations. Single ended contact formation (SECF) classifiers using only forces/torques measured at the wrist of the robot have been shown to be quite effective for this purpose. To function properly, however, previous SECF classifiers have required a sizable training set and a constant pose between the force/torque sensor and the manipulated object. Thus, if an object is regrasped and the pose changes, one expects to have to repeat the creation of the training set. We discuss the impact sensor-object pose changes have on two successful classifiers. Experimental data shows that they perform poorly when sensor-object pose changes. We derive, experimentally verify and discuss a method to regain the performance of both classifiers while minimizing the retraining necessary. Index Terms: Contact formation, force sensing, kinematics.

Published
1999

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