4 resultados para intellectual cooperation

em Massachusetts Institute of Technology


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The future of the software industry is today being shaped in the courtroom. Most discussions of intellectual property to date, however, have been frames as debates about how the existing law --- promulgated long before the computer revolution --- should be applied to software. This memo is a transcript of a panel discussion on what forms of legal protection should apply to software to best serve both the industry and society in general. After addressing that question we can consider what laws would bring this about.

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Over the next five years, computer games will find their way into a vast number of American homes, creating a unique educational opportunity: the development of "computer coaches" for the serious intellectual skills required by some of these games. From the player's perspective, the coach will provide advice regarding strategy and tactics for better play. But, from the perspective of the coach, the request for help is an opportunity to tutor basic mathematical, scientific or other kinds of knowledge that the game exercises.

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This report addresses the problem of achieving cooperation within small- to medium- sized teams of heterogeneous mobile robots. I describe a software architecture I have developed, called ALLIANCE, that facilitates robust, fault tolerant, reliable, and adaptive cooperative control. In addition, an extended version of ALLIANCE, called L-ALLIANCE, is described, which incorporates a dynamic parameter update mechanism that allows teams of mobile robots to improve the efficiency of their mission performance through learning. A number of experimental results of implementing these architectures on both physical and simulated mobile robot teams are described. In addition, this report presents the results of studies of a number of issues in mobile robot cooperation, including fault tolerant cooperative control, adaptive action selection, distributed control, robot awareness of team member actions, improving efficiency through learning, inter-robot communication, action recognition, and local versus global control.

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