Advanced Approximation Methods and Specification Schemes for Automated Reasoning

本项目将探索推理和知识表示的三种途径： 开发新的近似方法，结合用户自适应和任何时间的特点; 开发混合知识库，将联合收割机确定性信息（约束）和概率性信息（信念网络）结合起来，并且在语义上连贯一致，在计算上有效; 以及混合语言和算法在规划、调度和诊断领域的时间推理问题中的应用。 这项研究的成果将包括一个系统的算法工具，解决问题的非易处理性的创新和实用的方式，并适用于一个新的知识为基础的框架，允许表达的因果关系和约束样的信息，从而促进任务，如规划，诊断和设计。 参数化将允许用户控制算法，并根据自己的域和资源进行调整。 这些计算工具将支持解决不同科学和工业领域前沿的挑战性问题，如机器人、规划和调度、生物信息学（连锁分析和蛋白质二级结构预测）和电子商务（多代理组合拍卖）。

This project will explore three avenues in reasoning and knowledge representation: development of new approximation methods that incorporate user-adaptive and any-time features; development of hybrid knowledge-bases that combine deterministic information (constraints) and probabilistic information (belief networks), and which are both semantically coherent and computationally effective; and application of hybrid languages and algorithms to temporal reasoning problems in the domains of planning, scheduling, and diagnosis. The outcome of this research will include a system of algorithmic tools which address issues of non-tractability in an innovative and practical manner, and which are applicable to a new knowledge-based framework that allows the expression of both causal and constraint-like information, thus facilitating tasks such as planning, diagnosis and design. Parameterization will allow users to control the algorithms and adjust them to their own domains and resources. The computational tools will support the solution of challenging problems at the frontiers of diverse areas of science and industry such as robotics, planning and scheduling, bioinformatics (linkage analysis and protein secondary structure prediction), and e-commerce (multi-agent combinatorial auctions).