Strategies for High Performance Graph-Based Reasoning

该项目旨在为高性能基于图的推理系统开发自适应技术，使用户能够控制计算资源和解决方案质量之间的权衡。这个项目的主旨是在约束优化、概率推理和不确定性决策的算法中引入适应性和可扩展性。该项目分为几个子项目，研究：（1）图形模型的迭代置信传播;（2）随机局部搜索和推理的混合;（3）基于分区的搜索指导;（4）混合概率和确定性（约束）网络。这些子项目被PI正在进行的关于“参数化有界推理”统一框架的研究联系在一起，该框架结合了搜索和基于结构的推理两种范式。赋予基于图的算法以更强的适应性和可扩展性不仅对人工智能和计算机科学的进步很重要，而且对许多领域的应用也很重要。该项目的另一个目标是将开发的算法封装在一个软件推理和评估外壳（REES）中，以允许统一的经验评估，并促进研究人员，教育工作者和应用程序构建者传播该项目的结果。

This project seeks to develop adaptive techniques for high performance graph-based reasoning systems that allow users to control the tradeoffs between computational resources and solution quality. The main thrust of this project is to introduce adaptability and scalability in algorithms for constraint optimization, probabilistic inference, and decision making under uncertainty. The project is structured into subprojects that study: (1) iterative belief propagation for graphical models; (2) hybrids of stochastic local search and inference; (3) search guided by partition-based heuristics; and (4) mixed probabilistic and deterministic (constraint) networks. These subprojects are tied together by the PI's ongoing research on the unifying framework of "parameterized bounded inference" that combines the two paradigms of search and structure-based inference. Endowing graph-based algorithms with increased adaptability and scalability is important not only to progress in AI and computer science but also to application in many domains. An additional goal of this project is to package the developed algorithms in one software reasoning and evaluation shell (REES) to allow uniform empirical evaluation and to facilitate dissemination of the project's results by researchers, educators and application builders.