Robustness from Non-Stop Collaboration

提案0534978“不间断协作的稳健性“PI：帕特里克H。WinstonMITThis提案旨在了解如何从模块中产生强大的行为，这些模块共同开发描述并在超出任何一个模块单独操作能力的水平上合作解决问题。例如，人类的语言和视觉系统合作形成了一个世界观，这超出了任何一个单独产生的。该项目的一个关键方面是了解必要的模块间通信能力如何随着模块在其各自能力上的增长而增长，以及这种通信如何能够容忍通信中的不完善。本研究通过开发可组合的模块间通道来解决这个问题，使用两个使能的想法：融合地图和自配置通信通道。融合图使用约束传播，提升到模块之间和跨表示操作，以确定模块之间的语义对应关系，并支持知识从信息更确定的模块到信息不太确定的模块的双向转移。自配置通信通道从共享的经验中学习模块之间的约束;这个想法解决了两个问题，即通道何时应该尝试学习以及如何实际学习有用的等价物。该项目将研究这些想法，探讨如何从不同传感器收集的数据中形成活动的复合记录（例如，图像、声音），例如在可能存在各种活动的办公室领域中。除了深入了解智能的本质外，这项研究还将提高计算机系统的鲁棒性，这在从控制危险设备到减轻自然灾害的许多情况下都很重要。

Proposal 0534978"Robustness from Non-Stop Collaboration"PI: Patrick H. WinstonMITThis proposal seeks to understand how robust behavior can emerge from modules that collectively develop descriptions and cooperatively solve problems at a level beyond the ability of any one module operating individually. For instance, human language and vision systems cooperate to form a world view that is beyond what either could produce individually. A key aspect of this project is to understand how the necessary intermodular communication capabilities grow as the modules grow in their individual capabilities, and how such communication can be tolerant of imperfections in the communications. This research approaches this problem through development of composable intermodular channels, using two enabling ideas: fusion maps and self-configuring communication channels. Fusion maps use constraint propagation, elevated to operate between modules and across representations, to determine semantic correspondences between modules and to support bi-directional transfer of knowledge from a module that is more certain of information to those that are less so. Self-configuring communication channels learn constraints between modules from shared experiences; this idea addresses the twin problems of when should a channel attempt to learn and how can useful equivalences actually be learned. The project will investigate these ideas on the problem of forming a composite record of activities from data collected from a diverse set of sensors (e.g., images, sound), for instance, in an office domain where there can be a variety of activities. Besides giving insight into the nature of intelligence, this research will improve the robustness of computer systems, important in many contexts from the control of dangerous equipment to the mitigation of natural disasters.