Advanced Approximation Methods and Specification Schemes for Automated Reasoning

这个项目将探索推理和知识表示的三个途径：开发结合了用户自适应和任何时间特征的新的近似方法；开发结合确定性信息(约束)和概率信息(信念网络)的混合知识库，并且在语义上连贯和计算有效；以及将混合语言和算法应用于计划、调度和诊断领域的时间推理问题。这项研究的结果将包括一个算法工具系统，它以创新和实用的方式解决不可处理性问题，并适用于一个新的基于知识的框架，该框架允许表达因果信息和类似约束的信息，从而促进规划、诊断和设计等任务。参数化将允许用户控制算法并将其调整为自己的域和资源。计算工具将支持解决科学和工业不同领域的前沿挑战问题，如机器人、规划和调度、生物信息学(链接分析和蛋白质二级结构预测)和电子商务(多智能体组合拍卖)。

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).