Advanced Approximation Methods and Specification Schemes for Automated Reasoning

该项目将探索推理和知识表示的三个途径：开发包含用户自适应和任意时间特征的新近似方法；结合确定性信息（约束）和概率信息（信念网络）的混合知识库的开发，既具有语义上的一致性，又具有计算效率；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。这项研究的结果将包括一个算法工具系统，该系统以创新和实用的方式解决不可处理的问题，并适用于一个新的基于知识的框架，该框架允许表达因果和约束类信息，从而促进诸如规划、诊断和设计等任务。参数化将允许用户控制算法并调整它们以适应自己的领域和资源。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、计划和调度、生物信息学（链接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).