REU SUPPLEMENT: Parallel Processing Projects in Artificial Intelligence

该奖项提供支持，以开发计算基础设施， 各种各样的研究，主要领域是： 人工智能系统的信念归属;复杂的符号信息 连接系统中的处理.连接系统的相联网络数据库 计算机视觉;模型生成推理系统;并行 场景模型生成算法。 新基建包括 将并行处理器从4处理器系统升级到22处理器系统 处理器系统，购置了一些工作站， 提供硬件和软件技术人员。 在关于自然语言话语的信念/隐喻项目中， 理解，两种互补的方法来整合 隐喻的加工和信息的加工 并探讨参加者的信念。 在一种方法中， 处理的简单扩展的信念推理机在一个 现有的程序，而另一种方法基于表示 的信念状态的常识隐喻模型， 常常在演讲中被利用。 在联结主义项目中，联结主义信息处理 正在开发的技术在高级认知中是有用的， 任务，如自然语言理解和常识 推理，而不是感知和适应任务， 大多数联结主义的研究都是针对 部分工作是 利用网络中发生的信号定时效应， 真实的生物神经网络。 视觉数据库项目的目的是设计，实现， 测试数据库（适用于机器人应用程序），支持 关联组织和检索，按物理聚类 属性，更高的抽象级别，以及高效的搜索 匹配实体。 所使用的范例是Pathfinder的扩展 网络（最初旨在模拟人类语义记忆）称为 单调搜索网络，这是一种邻近图。 模型生成推理（Model Generative Reasoning）是一种通用的自动化问题求解方法 由Coombs和Hartley开发。 这是个专家问题 解决方案强大的根据冲突的证据，通过 在适当的溯因/演绎中保持备择假设 周期 在场景模型生成项目的并行算法中，时间- 正在分析基于自然场景的各种图像， 开发场景的动态三维模型。 这项工作的重要特点包括一个假设和测试范式 对于模型的构建，通过 局部一致性约束的使用和模型的维护 通过数据锁定协议实现并行环境中的一致性 从图形语法产品自动开发。

This award provides support to develop computing infrastructure for a wide variety of research with the primary areas being: plausible ascription of beliefs by an AI system; complex symbolic information processing in connectionist systems; associative network databases for computer vision; the Model Generative Reasoning system; and parallel algorithms for scene model generation. The new infrastructure includes the upgrading of a parallel processor from a 4 processor system to a 22 processor system, the acquisition of a number of workstations, and the provision of hardware and software technicians. In the Beliefs/Metaphor project on natural language discourse understanding, two complementary approaches to integrating the processing of metaphors and the processing of information about participants'belief are being explored. In one approach, metaphors are processed by a simple extension of the belief-reasoning machinery in an existing program, whereas the other approach bases the representation of belief states on common-sense metaphorical models of mind that are often exploited in discourse. In the Connectionism project, connectionist information-processing techniques are being developed that are useful in high-level cognitive tasks, such as natural language understanding and common-sense reasoning, as opposed to the perceptual and adaptation tasks to which most connectionist work has been directed. Part of the work is exploiting signal timing effects that occur in networks inspired by real biological neural networks. The purpose of the vision database project is to design, implement, and test a database (appropriate for robotics applications) which supports associative organization and retrieval, clustering by physical properties, higher levels of abstraction, and efficient search for matching entities. The paradigm used is an extension of Pathfinder networks (originally intended to model human semantic memory) called monotonic search networks, which is a type of proximity graph. Model Generative Reasoning is a general automated problem solving system developed by Coombs and Hartley. It makes expert problem solvers robust in the light of conflicting evidence through the maintenance of alternative hypotheses in a proper abduction/deduction cycle. In the Parallel Algorithms for Scene Model Generation project, time- varying imagery based on natural scenes is being analyzed with the goal of developing dynamic three-dimensional models of the scenes. Important features of the work include a hypothesis-and-test paradigm for construction of the models, merging of multiple cues through the use of local consistency constraints, and maintenance of model consistency in a parallel environment through data locking protocols developed automatically from graph grammar productions.