Constraint Solving and Matching: Parallel Algorithms and Applications

对灵活高效知识的追求 表征机制是信息系统中的关键研究问题之一， 人工智能（AI）。 复形的瞬时匹配 对大量预定义对象的对象描述是关键 在许多智能系统中的计算原语。 此函数 基于记忆的推理表现在不同的活动中， 例如计算机视觉中的模式识别任务、语言 理解或解决一般问题。 同时 满足许多可能冲突的约束是另一个 智能系统中非常重要的功能需求， 对于学习、规划和解决一般问题至关重要。 它 一般认为人类能有效地完成这些任务 (i.e.,在几乎恒定的时间内）， 在大脑里。 这项研究旨在获得一个精确的 基本人工智能中并行性效用的表征 约束求解和匹配等问题。 最终 研究的目标是建立一个计算框架， 并行智能系统 具体的研究目标是 约束网络的高效并行算法设计， 开发集成多约束求解的方法 广义匹配和约束的活动和应用 解决反应性计划、资源分配和分析 科学数据。

The quest for a flexible and efficient knowledge representation mechanism is one of the key research problems in Artificial Intelligence (AI). Instantaneous matching of a complex object description to a large set of predefined objects is a key computational primitive in many intelligent systems. This function of memory based reasoning manifests itself in different activities such as pattern recognition tasks in computer vision, language understanding or general problem solving. Simultaneous satisfaction of many possibly conflicting constraints is another very important functional requirement in intelligent systems, which is critical for learning, planning and general problem solving. It is generally believed that humans perform such tasks efficiently (i.e., in almost constant time) by exploiting massive parallelism in the brain. This research is aimed at deriving a precise characterization of the utility of parallelism in fundamental AI problems such as constraint solving and matching. The ultimate goal of the research is to produce a computational framework for parallel intelligent systems. Specific research objectives are design of efficient parallel algorithms for constraint networks, development of methods to integrate multiple constraint solving activities, and applications of generalized matching and constraint solving to reactive planning, resource allocation and analysis of scientific data.//